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    <title>[2412.05266] Locomotion of a Scallop-Inspired Swimmer in Granular Matter</title>
    <dc:date>2025-08-22T13:01:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2412.05266</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Understanding swimming in soft yielding media is challenging due to their complex deformation response to the swimmer's motion. We experimentally show that a scallop-inspired swimmer with reciprocally flapping wings generates locomotion in granular matter. This disagrees with the scallop theorem prohibiting reciprocal swimming in a liquid when its inertia is negligible. We use X-ray tomography and laser profilometry to show that the propulsion is created by the combined effects of jamming and convection of particles near the wings, which break the symmetry in packing density, surface deformation, and kinematics of the granular medium between an opening and a closing stroke.
]]></description>
<dc:subject>biologically-inspired engineering-design looking-to-see granular-materials nonlinear-dynamics rather-interesting robotics nanotechnology fluid-dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3d766bc18679/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2309.11470">
    <title>[2309.11470] Model-free tracking control of complex dynamical trajectories with machine learning</title>
    <dc:date>2025-04-05T22:41:50+00:00</dc:date>
    <link>https://arxiv.org/abs/2309.11470</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing. Stochastic input is exploited for training, which consists of the observed partial state vector as the first and its immediate future as the second component so that the neural machine regards the latter as the future state of the former. In the testing (deployment) phase, the immediate-future component is replaced by the desired observational vector from the reference trajectory. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals, and establish its robustness against measurement noise, disturbances, and uncertainties.
]]></description>
<dc:subject>control-theory nonlinear-dynamics robotics rather-interesting to-write-about to-simulate consider:Lyaponov-exponents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9e2243ef336d/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2302.05517">
    <title>[2302.05517] Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks</title>
    <dc:date>2024-11-01T19:08:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2302.05517</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we experimentally examine the cognitive capability of a simple, paper-based Miura-ori -- using the physical reservoir computing framework -- to achieve different information perception tasks. The body dynamics of Miura-ori (aka. its vertices displacements), which is excited by a simple harmonic base excitation, can be exploited as the reservoir computing resource. By recording these dynamics with a high-resolution camera and image processing program and then using linear regression for training, we show that the origami reservoir has sufficient computing capacity to estimate the weight and position of a payload. It can also recognize the input frequency and magnitude patterns. Furthermore, multitasking is achievable by simultaneously applying two targeted functions to the same reservoir state matrix. Therefore, we demonstrate that Miura-ori can assess the dynamic interactions between its body and ambient environment to extract meaningful information -- an intelligent behavior in the mechanical domain. Given that Miura-ori has been widely used to construct deployable structures, lightweight materials, and compliant robots, enabling such information perception tasks can add a new dimension to the functionality of such a versatile structure.
]]></description>
<dc:subject>reservoir-computing distributed-processing origami unconventional-computing rather-interesting robotics to-understand to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83ef83f16764/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2309.10201">
    <title>[2309.10201] Evolving generalist controllers to handle a wide range of morphological variations</title>
    <dc:date>2024-09-21T14:03:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2309.10201</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalizability of the controllers. This is achieved by introducing morphological variations during the evolutionary training process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalizability and proposes a method by which to improve these properties.
]]></description>
<dc:subject>artificial-life robotics generalization overfitting evolutionary-algorithms rather-interesting to-write-about to-simulate</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:99650ce0cd60/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2109.04175">
    <title>[2109.04175] Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments</title>
    <dc:date>2024-08-08T13:31:43+00:00</dc:date>
    <link>https://arxiv.org/abs/2109.04175</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Ensuring safe behavior for automated vehicles in unregulated traffic areas poses a complex challenge for the industry. It is an open problem to provide scalable and certifiable solutions to this challenge. We derive a trajectory planner based on model predictive control which interoperates with a monitoring system for pedestrian safety based on cellular automata. The combined planner-monitor system is demonstrated on the example of a narrow indoor parking environment. The system features deterministic behavior, mitigating the immanent risk of black boxes and offering full certifiability. By using fundamental and conservative prediction models of pedestrians the monitor is able to determine a safe drivable area in the partially occluded and unstructured parking environment. The information is fed to the trajectory planner which ensures the vehicle remains in the safe drivable area at any time through constrained optimization. We show how the approach enables solving plenty of situations in tight parking garage scenarios. Even though conservative prediction models are applied, evaluations indicate a performant system for the tested low-speed navigation.
]]></description>
<dc:subject>operations-research machine-learning rather-interesting planning computational-geometry online-learning online-planning robotics death-machines</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5b45c9fa4c7a/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2209.10687">
    <title>[2209.10687] Motion Planning for a Climbing Robot with Stochastic Grasps</title>
    <dc:date>2023-10-10T10:14:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2209.10687</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Motion planning for a multi-limbed climbing robot must consider the robot's posture, joint torques, and how it uses contact forces to interact with its environment. This paper focuses on motion planning for a robot that uses nontraditional locomotion to explore unpredictable environments such as martian caves. Our robotic concept, ReachBot, uses extendable and retractable booms as limbs to achieve a large reachable workspace while climbing. Each extendable boom is capped by a microspine gripper designed for grasping rocky surfaces. ReachBot leverages its large workspace to navigate around obstacles, over crevasses, and through challenging terrain. Our planning approach must be versatile to accommodate variable terrain features and robust to mitigate risks from the stochastic nature of grasping with spines. In this paper, we introduce a graph traversal algorithm to select a discrete sequence of grasps based on available terrain features suitable for grasping. This discrete plan is complemented by a decoupled motion planner that considers the alternating phases of body movement and end-effector movement, using a combination of sampling-based planning and sequential convex programming to optimize individual phases. We use our motion planner to plan a trajectory across a simulated 2D cave environment with at least 95% probability of success and demonstrate improved robustness over a baseline trajectory. Finally, we verify our motion planning algorithm through experimentation on a 2D planar prototype.
]]></description>
<dc:subject>motion-planning robotics simulation rather-interesting adaptive-control control-theory to-understand to-visualize</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b45bfd4b7b0a/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1610.04080">
    <title>[1610.04080] Cuspidal Robots</title>
    <dc:date>2022-05-12T11:01:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.04080</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This chapter is dedicated to the so-called cuspidal robots, i.e. those robots that can move from one inverse geometric solution to another without meeting a singular confuguration. This feature was discovered quite recently and has then been fascinating a lot of researchers. After a brief history of cuspidal robots, the chapter provides the main features of cuspidal robots: explanation of the non-singular change of posture, uniqueness domains, regions of feasible paths, identification and classification of cuspidal robots. The chapter focuses on 3-R orthogonal serial robots. The case of 6-dof robots and parallel robots is discussed in the end of this chapter.
]]></description>
<dc:subject>robotics inverse-problems rather-interesting constraint-satisfaction to-understand kinematics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3316f50a74f2/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2106.03911">
    <title>[2106.03911] XIRL: Cross-embodiment Inverse Reinforcement Learning</title>
    <dc:date>2022-03-09T17:35:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.03911</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We investigate the visual cross-embodiment imitation setting, in which agents learn policies from videos of other agents (such as humans) demonstrating the same task, but with stark differences in their embodiments -- shape, actions, end-effector dynamics, etc. In this work, we demonstrate that it is possible to automatically discover and learn vision-based reward functions from cross-embodiment demonstration videos that are robust to these differences. Specifically, we present a self-supervised method for Cross-embodiment Inverse Reinforcement Learning (XIRL) that leverages temporal cycle-consistency constraints to learn deep visual embeddings that capture task progression from offline videos of demonstrations across multiple expert agents, each performing the same task differently due to embodiment differences. Prior to our work, producing rewards from self-supervised embeddings typically required alignment with a reference trajectory, which may be difficult to acquire under stark embodiment differences. We show empirically that if the embeddings are aware of task progress, simply taking the negative distance between the current state and goal state in the learned embedding space is useful as a reward for training policies with reinforcement learning. We find our learned reward function not only works for embodiments seen during training, but also generalizes to entirely new embodiments. Additionally, when transferring real-world human demonstrations to a simulated robot, we find that XIRL is more sample efficient than current best methods. Qualitative results, code, and datasets are available at this https URL
]]></description>
<dc:subject>machine-learning learning-by-watching reinforcement-learning rather-interesting robotics embodied-systems engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d3fae393fc31/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2108.08449">
    <title>[2108.08449] A cut-and-fold self-sustained compliant oscillator for autonomous actuation of origami-inspired robots</title>
    <dc:date>2022-01-29T14:03:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2108.08449</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Origami-inspired robots are of particular interest given their potential for rapid and accessible design and fabrication of elegant designs and complex functionalities through cutting and folding of flexible 2D sheets or even strings, i.e.printable manufacturing. Yet, origami robots still require bulky, rigid components or electronics for actuation and control to accomplish tasks with reliability, programmability, ability to output substantial force, and durability, restricting their full potential. Here, we present a printable self-sustained compliant oscillator that generates periodic actuation using only constant electrical power, without discrete components or electronic control hardware. This oscillator is robust (9 out of 10 prototypes worked successfully on the first try), configurable (with tunable periods from 3 s to 12 s), powerful (can overcome hydrodynamic resistance to consistently propel a swimmer at ~1.6 body lengths/min), and long-lasting (~10^3 cycles); it enables driving macroscale devices with prescribed autonomous behaviors, e.g. locomotion and sequencing. This oscillator is also fully functional underwater and in high magnetic fields. Our analytical model characterizes essential parameters of the oscillation period, enabling programmable design of the oscillator. The printable oscillator can be integrated into origami-inspired systems seamlessly and monolithically, allowing rapid design and prototyping; the resulting integrated devices are lightweight, low-cost, compliant, electronic-free, and nonmagnetic, enabling practical applications in extreme areas. We demonstrate the functionalities of the oscillator with: (i) autonomous gliding of a printable swimmer, (ii) LED flashing, and (iii) fluid stirring. This work paves the way for realizing fully printable autonomous robots with a high integration of actuation and control.
]]></description>
<dc:subject>robotics origami engineering-design rather-interesting 3d-printing nonstandard-electronics mechanics kinematics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cd9063040f52/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origami"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d-printing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonstandard-electronics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinematics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.04735">
    <title>[1810.04735] Towards the Targeted Environment-Specific Evolution of Robot Components</title>
    <dc:date>2021-11-04T10:28:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.04735</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.
]]></description>
<dc:subject>artificial-life genetic-programming robotics rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:721f870d8405/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.02579">
    <title>[1904.02579] Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography</title>
    <dc:date>2021-06-17T21:57:11+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.02579</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Aerial filming is constantly gaining importance due to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose a deep reinforcement learning agent which supervises motion planning of a filming drone by making desirable shot mode selections based on aesthetical values of video shots. Unlike most of the current state-of-the-art approaches that require explicit guidance by a human expert, our drone learns how to make favorable viewpoint selections by experience. We propose a learning scheme that exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both a hand-crafted reward function as well as reward from direct human input. We then deploy the same agent on a real DJI M210 drone in order to test the generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shot quality of our methods. Videos of the system in action can be seen at this https URL.
]]></description>
<dc:subject>robotics dromes machine-learning aesthetics learning-by-watching rather-interesting consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2b5912f590ad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dromes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ucl.ac.uk/news/2021/may/robotic-third-thumb-use-can-alter-brain-representation-hand">
    <title>Robotic ‘Third Thumb’ use can alter brain representation of the hand | UCL News - UCL – University College London</title>
    <dc:date>2021-05-25T13:11:22+00:00</dc:date>
    <link>https://www.ucl.ac.uk/news/2021/may/robotic-third-thumb-use-can-alter-brain-representation-hand</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Robotic ‘Third Thumb’ use can alter brain representation of the hand]]></description>
<dc:subject>robotics cyborgs now-I-want-more-thumbs neohomunculus</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:dcdc64e20870/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cyborgs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:now-I-want-more-thumbs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neohomunculus"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openai.com/blog/learning-dexterity/">
    <title>Learning Dexterity</title>
    <dc:date>2020-05-14T11:43:30+00:00</dc:date>
    <link>https://openai.com/blog/learning-dexterity/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What surprised us
Tactile sensing is not necessary to manipulate real-world objects. Our robot receives only the locations of the five fingertips along with the position and orientation of the cube. Although the robot hand has touch sensors on its fingertips, we didn’t need to use them. Generally, we found better performance from using a limited set of sensors that could be modeled effectively in the simulator instead of a rich sensor set with values that were hard to model.
Randomizations developed for one object generalize to others with similar properties. After developing our system for the problem of manipulating a block, we printed an octagonal prism, trained a new policy using its shape, and attempted to manipulate it. Somewhat to our surprise, it achieved high performance using only the randomizations we had designed for the block. By contrast, a policy that manipulated a sphere could only achieve a few successes in a row, perhaps because we had not randomized any simulation parameters that model rolling behavior.
With physical robots, good systems engineering is as important as good algorithms. At one point, we noticed that one engineer consistently achieved much better performance than others when running the exact same policy. We later discovered that he had a faster laptop, which hid a timing bug that reduced performance. After the bug was fixed, performance improved for the rest of the team.
]]></description>
<dc:subject>robotics machine-learning situated-learning rather-interesting artificial-life algorithms to-write-about consider:simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6d75c2000e2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:situated-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1803.08202">
    <title>[1803.08202] Person Following by Autonomous Robots: A Categorical Overview</title>
    <dc:date>2019-11-02T12:50:11+00:00</dc:date>
    <link>https://arxiv.org/abs/1803.08202</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, some of the prominent methods are qualitatively compared, corresponding practicalities are illustrated, and their feasibility is analyzed for various use-cases. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.
]]></description>
<dc:subject>robotics review rather-interesting goal-balancing planning algorithms experiment looking-to-see machine-learning adaptive-control adaptive-behavior to-write-about image-processing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b54b85280659/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:review"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:goal-balancing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adaptive-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adaptive-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1803.02015">
    <title>[1803.02015] Generative Modeling of Multimodal Multi-Human Behavior</title>
    <dc:date>2019-09-08T14:50:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1803.02015</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i.e. where there are many possible highly-distinct futures). A motivating example includes robots interacting with humans in crowded environments, such as self-driving cars operating alongside human-driven vehicles or human-robot collaborative bin packing in a warehouse. Our approach to model human behavior in such uncertain environments is to model humans in the scene as nodes in a graphical model, with edges encoding relationships between them. For each human, we learn a multimodal probability distribution over future actions from a dataset of multi-human interactions. Learning such distributions is made possible by recent advances in the theory of conditional variational autoencoders and deep learning approximations of probabilistic graphical models. Specifically, we learn action distributions conditioned on interaction history, neighboring human behavior, and candidate future agent behavior in order to take into account response dynamics. We demonstrate the performance of such a modeling approach in modeling basketball player trajectories, a highly multimodal, multi-human scenario which serves as a proxy for many robotic applications.
]]></description>
<dc:subject>swarms generative-models robotics to-understand performance-measure consider:performance-measures consider:genetic-programming to-simulate collective-behavior planning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6a22d37a283a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1403.2882">
    <title>[1403.2882] A Fibonacci control system with application to hyper-redundant manipulators</title>
    <dc:date>2019-08-06T22:32:46+00:00</dc:date>
    <link>https://arxiv.org/abs/1403.2882</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study a robot snake model based on a discrete linear control system involving Fibonacci sequence and closely related to the theory of expansions in non-integer bases. The present paper includes an investigation of the reachable workspace, a more general analysis of the control system underlying the model, its reachability and local controllability properties and the relation with expansions in non-integer bases and with iterated function systems.
]]></description>
<dc:subject>control-theory robotics rather-odd to-understand the-words-and-pictures-are-confusing?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c381c27a2f9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-words-and-pictures-are-confusing?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1710.04459">
    <title>[1710.04459] Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions</title>
    <dc:date>2019-04-24T15:03:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1710.04459</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the paradigm of a black box AI system that makes life-critical decisions. We propose an "arguing machines" framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task. We show that disagreement between the two systems, without any knowledge of underlying system design or operation, is sufficient to arbitrarily improve the accuracy of the overall decision pipeline given human supervision over disagreements. We demonstrate this system in two applications: (1) an illustrative example of image classification and (2) on large-scale real-world semi-autonomous driving data. For the first application, we apply this framework to image classification achieving a reduction from 8.0% to 2.8% top-5 error on ImageNet. For the second application, we apply this framework to Tesla Autopilot and demonstrate the ability to predict 90.4% of system disengagements that were labeled by human annotators as challenging and needing human supervision.
]]></description>
<dc:subject>argumentation collective-intelligence rather-interesting adversarial-behavior robotics performance-measure to-write-about to-understand coevolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49a668b1340f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adversarial-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coevolution"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.degruyter.com/view/j/pjbr.2013.4.issue-1/pjbr-2013-0003/pjbr-2013-0003.xml">
    <title>Robot Skill Learning: From Reinforcement Learning to Evolution Strategies : Paladyn, Journal of Behavioral Robotics</title>
    <dc:date>2018-04-02T11:38:41+00:00</dc:date>
    <link>https://www.degruyter.com/view/j/pjbr.2013.4.issue-1/pjbr-2013-0003/pjbr-2013-0003.xml</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. Owing to current trends involving searching in parameter space (rather than action space) and using reward-weighted averaging (rather than gradient estimation), reinforcement learning algorithms for policy improvement, e.g. PoWER and PI2, are now able to learn sophisticated high-dimensional robot skills. A side-effect of these trends has been that, over the last 15 years, reinforcement learning (RL) algorithms have become more and more similar to evolution strategies such as (μW , λ)-ES and CMA-ES. Evolution strategies treat policy improvement as a black-box optimization problem, and thus do not leverage the problem structure, whereas RL algorithms do. In this paper, we demonstrate how two straightforward simplifications to the state-of-the-art RL algorithm PI2 suffice to convert it into the black-box optimization algorithm (μW, λ)-ES. Furthermore, we show that (μW , λ)-ES empirically outperforms PI2 on the tasks in [36]. It is striking that PI2 and (μW , λ)-ES share a common core, and that the simpler algorithm converges faster and leads to similar or lower final costs. We argue that this difference is due to a third trend in robot skill learning: the predominant use of dynamic movement primitives (DMPs). We show how DMPs dramatically simplify the learning problem, and discuss the implications of this for past and future work on policy improvement for robot skill learning

]]></description>
<dc:subject>robotics machine-learning metaheuristics algorithms learning-by-doing engineering-design planning to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d44d476a4be3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1802.08995">
    <title>[1802.08995] Using Information Invariants to Compare Swarm Algorithms and General Multi-Robot Algorithms: A Technical Report</title>
    <dc:date>2018-03-31T12:31:51+00:00</dc:date>
    <link>https://arxiv.org/abs/1802.08995</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Robotic swarms are decentralized multi-robot systems whose members use local information from proximal neighbors to execute simple reactive control laws that result in emergent collective behaviors. In contrast, members of a general multi-robot system may have access to global information, all- to-all communication or sophisticated deliberative collabora- tion. Some algorithms in the literature are applicable to robotic swarms. Others require the extra complexity of general multi- robot systems. Given an application domain, a system designer or supervisory operator must choose an appropriate system or algorithm respectively that will enable them to achieve their goals while satisfying mission constraints (e.g. bandwidth, energy, time limits). In this paper, we compare representative swarm and general multi-robot algorithms in two application domains - navigation and dynamic area coverage - with respect to several metrics (e.g. completion time, distance trav- elled). Our objective is to characterize each class of algorithms to inform offline system design decisions by engineers or online algorithm selection decisions by supervisory operators. Our contributions are (a) an empirical performance comparison of representative swarm and general multi-robot algorithms in two application domains, (b) a comparative analysis of the algorithms based on the theory of information invariants, which provides a theoretical characterization supported by our empirical results.]]></description>
<dc:subject>swarms robotics planning collective-intelligence information-theory feature-construction rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b5a97da38496/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.00264">
    <title>[1704.00264] Potential Functions based Sampling Heuristic For Optimal Path Planning</title>
    <dc:date>2017-09-29T13:56:12+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.00264</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacle's geometry in a given environment. However, one of the limitations in the RRT* algorithm is slow convergence to optimal path solution. As a result, it consumes high memory as well as time due to a large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the Potential Function Based-RRT* (P-RRT*) that incorporates the Artificial Potential Field Algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.
]]></description>
<dc:subject>planning representation robotics computational-geometry heuristics rather-interesting to-write-about consider:looking-to-see consider:simplifying</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:55a4737c5fa2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:simplifying"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.birds.eecs.umich.edu/">
    <title>BIRDS-Lab</title>
    <dc:date>2017-05-26T21:50:18+00:00</dc:date>
    <link>http://www.birds.eecs.umich.edu/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Professor Revzen and his team at the Biologically Inspired Robotics and Dynamical Systems (BIRDS) Lab are working on discovering, modeling, and reproducing the strategies animals use when interacting with physical objects. This work consists of collaboration with biomechanists to analyze experimental data, developing new mathematical tools for modeling and estimation of model parameters, and construction of robots which employ the new principles.

]]></description>
<dc:subject>hey-I-know-this-guy robotics engineering-design engineering nudge-targets to-write-about local</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1f59b72d636f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:local"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.00695">
    <title>[1608.00695] The blockchain: a new framework for robotic swarm systems</title>
    <dc:date>2017-03-31T12:39:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.00695</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.
]]></description>
<dc:subject>robotics blockchain algorithms swarms a-bit-odd to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2667f04d30d6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:blockchain"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:a-bit-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1606.06329">
    <title>[1606.06329] Recognizing Surgical Activities with Recurrent Neural Networks</title>
    <dc:date>2017-03-24T13:02:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.06329</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at this https URL .]]></description>
<dc:subject>neural-networks recurrent-networks control-theory robotics machine-learning rather-interesting representation nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:96398121ead9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:recurrent-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1605.07493">
    <title>[1605.07493] Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control</title>
    <dc:date>2017-03-24T13:00:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1605.07493</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Road traffic crashes have been the leading cause of death among young people. Most of these accidents occur when the driver becomes distracted due to fatigue or external factors. Vehicle platooning systems such as Cooperative Adaptive Cruise Control (CACC) are one of the results of the effort devoted to the development of technologies for decreasing the number of road crashes and fatalities. Previous studies have suggested such systems improve up to 273\% highway traffic throughput and fuel consumption in more than 15\% if the clearance between vehicles in this class of roads can be reduced to 2 meters. This paper proposes an approach that guarantees a minimum safety distance between vehicles taking into account the overall system delays and braking capacity of each vehicle. A l∞-norm Robust Model Predictive Controller (RMPC) is developed to guarantee the minimum safety distance is not violated due to uncertainties on the lead vehicle behavior. A formulation for a lower bound clearance of vehicles inside a platoon is also proposed. Simulation results show the performance of the proposed approach compared to a nominal controller when the system is subject to both modeled and unmodeled disturbances.
]]></description>
<dc:subject>robotics operations-research planning algorithms performance-measure rather-interesting engineering-design nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:235f1d90683e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1606.07312">
    <title>[1606.07312] Unsupervised preprocessing for Tactile Data</title>
    <dc:date>2017-03-11T13:23:39+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.07312</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an unsupervised learning algorithm that transforms the complex tactile data into a compact, latent representation without the need to record ground truth reference data. These compact representations can either be used directly in a reinforcement learning based controller or can be used to calibrate the tactile sensor to physical quantities with only a few datapoints. We show the quality of our latent representation by predicting important features and with a simple control task.
]]></description>
<dc:subject>rather-interesting sensors robotics clustering machine-learning nudge-targets consider:looking-to-see consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c2714aa35823/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1701.09180">
    <title>[1701.09180] Deep Stochastic Radar Models</title>
    <dc:date>2017-02-28T12:39:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1701.09180</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes. This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning connected to real-world data. The resulting model exhibits fundamental radar effects while remaining real-time capable.
]]></description>
<dc:subject>rather-interesting signal-processing radar self-driving-cars robotics machine-learning statistics inference engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a4663eeed29e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-driving-cars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1610.01243">
    <title>[1610.01243] On the Construction of Safe Controllable Regions for Affine Systems with Applications to Robotics</title>
    <dc:date>2017-01-06T12:52:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.01243</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper studies the problem of constructing in-block controllable (IBC) regions for affine systems. That is, we are concerned with constructing regions in the state space of affine systems such that all the states in the interior of the region are mutually accessible through the region's interior by applying uniformly bounded inputs. We first show that existing results for checking in-block controllability on given polytopic regions cannot be easily extended to address the question of constructing IBC regions. We then explore the geometry of the problem to provide a computationally efficient algorithm for constructing IBC regions. We also prove the soundness of the algorithm. We then use the proposed algorithm to construct safe speed profiles for different robotic systems, including fully-actuated robots, ground robots modeled as unicycles with acceleration limits, and unmanned aerial vehicles (UAVs). Finally, we present several experimental results on UAVs to verify the effectiveness of the proposed algorithm. For instance, we use the proposed algorithm for real-time collision avoidance for UAVs.
]]></description>
<dc:subject>planning robotics optimization performance-measure constraint-satisfaction rather-interesting nonlinear-dynamics nudge-targets consider:feature-discovery consider:stress-testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d5cf547d77cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:stress-testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1605.03036">
    <title>[1605.03036] 3LP: a linear 3D-walking model including torso and swing dynamics</title>
    <dc:date>2016-12-25T23:07:10+00:00</dc:date>
    <link>https://arxiv.org/abs/1605.03036</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we present a new model of biped locomotion which is composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to double support, this model has different actuation possibilities in the swing hip and stance ankle which could be widely used to produce different walking gaits. Without the need for numerical time-integration, closed-form solutions help finding periodic gaits which could be simply scaled in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such property is coming from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract high level template space. The proposed model produces human-like torque and ground reaction force profiles and thus, compared to point-mass models, it is more promising for precise control of humanoid robots. Despite being linear and lacking many other features of human walking like CoM excursion, knee flexion and ground clearance, we show that the proposed model can predict one of the main optimality trends in human walking, i.e. nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis still remain for future works.
]]></description>
<dc:subject>robotics simulation engineering-design nudge-targets consider:stress-testing consider:looking-to-see to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:327a444a35dc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:stress-testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.05497">
    <title>[1611.05497] Explicable Robot Planning as Minimizing Distance from Expected Behavior</title>
    <dc:date>2016-12-17T13:43:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.05497</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In order for robots to be integrated effectively into human work-flows, it is not enough to address the question of autonomy but also how their actions or plans are being perceived by their human counterparts. When robots generate task plans without such considerations, they may often demonstrate what we refer to as inexplicable behavior from the point of view of humans who may be observing it. This problem arises due to the human observer's partial or inaccurate understanding of the robot's deliberative process and/or the model (i.e. capabilities of the robot) that informs it. This may have serious implications on the human-robot work-space, from increased cognitive load and reduced trust in the robot from the human, to more serious concerns of safety in human-robot interactions. In this paper, we propose to address this issue by learning a distance function that can accurately model the notion of explicability, and develop an anytime search algorithm that can use this measure in its search process to come up with progressively explicable plans. As the first step, robot plans are evaluated by human subjects based on how explicable they perceive the plan to be, and a scoring function called explicability distance based on the different plan distance measures is learned. We then use this explicability distance as a heuristic to guide our search in order to generate explicable robot plans, by minimizing the plan distances between the robot's plan and the human's expected plans. We conduct our experiments in a toy autonomous car domain, and provide empirical evaluations that demonstrate the usefulness of the approach in making the planning process of an autonomous agent conform to human expectations.
]]></description>
<dc:subject>planning robotics performance-measure multiobjective-optimization readability interpretability constraint-satisfaction rather-interesting to-write-about nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e60f41582887/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:readability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interpretability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1610.01495">
    <title>[1610.01495] The Sensitivity of the Static Center of Pressure as a Criterion to assess Balancing Controllers Performance</title>
    <dc:date>2016-11-03T11:09:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.01495</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Legged locomotion has received increasing attention from the robotics community. Contacts stability plays a critical role in ensuring that robots do not fall and it is a key element of balancing and walking controllers. The Center of Pressure is a contact stability criterion that defines a point which must be kept strictly inside the support polygon in order to ensure postural stability. We introduce the concept of the sensitivity of the static center of pressure, roughly speaking the rate of change of the center of pressure with respect to the system equilibrium configurations, as an additional criterion to assess the robustness of the contact stability. We show how this new concept can be used as a metric to assess balancing controllers performance and we apply it to evaluate the performance of two different balancing controllers. The analytical analysis is performed on a simplified model and validated during balancing tasks on the iCub humanoid robot.
]]></description>
<dc:subject>robotics simulation performance-measure engineering-design nudge-targets consider:looking-to-see consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c419d9a0da59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.03013">
    <title>[1608.03013] Belief Space Planning Simplified: Trajectory-Optimized LQG (T-LQG) (Extended Report)</title>
    <dc:date>2016-10-29T10:45:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.03013</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Planning under motion and observation uncertainties requires solution of a stochastic control problem in the space of feedback policies. In this paper, we reduce the general (n^2+n)-dimensional belief space planning problem to an (n)-dimensional problem by obtaining a Linear Quadratic Gaussian (LQG) design with the best nominal performance. Then, by taking the underlying trajectory of the LQG controller as the decision variable, we pose a coupled design of trajectory, estimator, and controller design through a Non-Linear Program (NLP) that can be solved by a general NLP solver. We prove that under a first-order approximation and a careful usage of the separation principle, our approximations are valid. We give an analysis on the existing major belief space planning methods and show that our algorithm has the lowest computational burden. Finally, we extend our solution to contain general state and control constraints. Our simulation results support our design.
]]></description>
<dc:subject>robotics planning artificial-intelligence machine-learning optimization algorithms constraint-satisfaction nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bea83a5b3f83/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1609.04946">
    <title>[1609.04946] Robot Contact Task State Estimation via Action Grammars</title>
    <dc:date>2016-09-20T13:12:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1609.04946</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define state transitions in a Markov chain or finite state machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to state transitions that are false-positives. The focus of this paper is to perform high-level state estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-level 3D cartesian trajectories and converts them into a high level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or "texture" are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL state estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in high-level planning, programming by demonstration, and human-robot interaction to name a few.
]]></description>
<dc:subject>robotics representation introspection philosophy-of-engineering rather-interesting machine-learning nudge-targets to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f5d42df36d6d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:introspection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1609.04947">
    <title>[1609.04947] Robot Introspection via Wrench-based Action Grammars</title>
    <dc:date>2016-09-20T13:11:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1609.04947</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the senseplan- act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a principled methodology to bootstrap robot introspection for contact tasks. In effect, we are trying to answer the question, what did the robot do? To this end, we hypothesize that all noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by meaningfully segmenting the data and then encoding it. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming sets of words or sentences, such that each subtask is uniquely represented by a word set. Such sets can be classified using statistical or machine learning techniques. We use SVMs and Mondrian Forests to classify contacts tasks both in simulation and in real robots for one and dual arm scenarios showing the general robustness of the approach. The contribution of our work is the presentation of a simple but generalizable semantic scheme that enables a robot to understand its high level state. This verification mechanism can provide feedback for high-level planners or reasoning systems that use semantic descriptors as well. The code, data, and other supporting documentation can be found at: this http URL
]]></description>
<dc:subject>introspection robotics rather-interesting philosophy-of-engineering learning-by-watching nudge-targets consider:representation to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc60f5814546/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:introspection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1605.06853">
    <title>[1605.06853] Path Planning in Dynamic Environments with Adaptive Dimensionality</title>
    <dc:date>2016-07-25T20:14:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.06853</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.
]]></description>
<dc:subject>planning artificial-intelligence robotics nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d46e8aa5d714/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1407.3501">
    <title>[1407.3501] Robots that can adapt like animals</title>
    <dc:date>2015-11-09T00:18:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1407.3501</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury.
]]></description>
<dc:subject>robotics machine-learning adaptive-control adaptive-behavior artificial-life nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:031ab438b53a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adaptive-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adaptive-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.00496">
    <title>[1505.00496] Recent Developments in the Optimization of Space Robotics for Perception in Planetary Exploration</title>
    <dc:date>2015-11-07T18:02:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.00496</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The following paper reviews recent developments in the field of optimization of space robotics. The extent of focus of this paper is on the perception (robotic sense of analyzing surroundings) in space robots in the exploration of extra-terrestrial planets. Robots play a crucial role in exploring extra-terrestrial and planetary bodies. Their advantages are far from being counted on finger tips. With the advent of autonomous robots in the field of robotics, the role for space exploration has further hustled up. Optimization of such autonomous robots has turned into a necessity of the hour. Optimized robots tend to have a superior role in space exploration. With so many considerations to monitor, an optimized solution will nevertheless help a planetary rover perform better under tight circumstances. Keeping in view the above mentioned area, the paper describes recent developments in the optimization of autonomous extra-terrestrial rovers.
]]></description>
<dc:subject>engineering-design robotics evolutionary-algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4f5db2a8fa07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1503.04347">
    <title>[1503.04347] Mutual Visibility by Luminous Robots Without Collisions</title>
    <dc:date>2015-11-06T14:31:53+00:00</dc:date>
    <link>http://arxiv.org/abs/1503.04347</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Consider a finite set of identical computational entities that can move freely in the Euclidean plane operating in Look-Compute-Move cycles. Let p(t) denote the location of entity p at time t; entity p can see entity q at time t if at that time no other entity lies in the line segment p(t)q(t). We consider the basic problem called Mutual Visibility: starting from arbitrary distinct locations, within finite time the entities must reach, without collisions, a configuration where they all see each other. This problem must be solved by each entity autonomously executing the same algorithm. We study this problem in the "luminous robots" model; in this generalization of the standard model of oblivious robots, each entity, called "robot", has an externally visible persistent light which can assume colors from a fixed set. The case where the number of colors is c=1 corresponds to the classical model without lights. 
In this paper we investigate under what conditions luminous robots can solve Mutual Visibility without collisions and at what cost (i.e., with how many colors). We establish a spectrum of results, depending on the power of the adversary, on the number c of colors, and on the a-priori knowledge the robots have about the system. Among such results, we prove that Mutual Visibility can always be solved without collisions in SSynch with c=2 colors and in ASynch with c=3 colors. If an adversary can interrupt and stop a robot moving to its computed destination, Mutual Visibility is still always solvable without collisions in SSynch with c=3 colors, and, if the robots agree on the direction of one axis, also in ASynch. All the results are obtained constructively by means of novel protocols. 
As a byproduct of our solutions, we provide the first obstructed-visibility solutions to two classical problems for oblivious robots: Collision-less Convergence to a point and Circle Formation.
Co]]></description>
<dc:subject>luminous-robots nuff-said robotics collective-intelligence agent-based swarms planning engineering-design rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:187dfb2d7007/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:luminous-robots"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nuff-said"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.2882">
    <title>[1403.2882] A Fibonacci control system with application to hyper-redundant manipulators</title>
    <dc:date>2015-11-02T21:43:15+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.2882</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study a robot snake model based on a discrete linear control system involving Fibonacci sequence and closely related to the theory of expansions in non-integer bases. The present paper includes an investigation of the reachable workspace, a more general analysis of the control system underlying the model, its reachability and local controllability properties and the relation with expansions in non-integer bases and with iterated function systems.
]]></description>
<dc:subject>robotics control-theory optimization planning kinematics nudge-targets engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:34c706a193d6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1308.3689">
    <title>[1308.3689] Evolving a Behavioral Repertoire for a Walking Robot</title>
    <dc:date>2015-09-14T11:13:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1308.3689</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which com-bines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of con-trollers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.
]]></description>
<dc:subject>robotics evolutionary-algorithms behavior-driven-design rather-interesting nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:56d4586da032/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:behavior-driven-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.00256">
    <title>[1505.00256] DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving</title>
    <dc:date>2015-09-12T21:05:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.00256</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception based approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network (CNN) using 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. Finally, we also train another CNN for car distance estimation on the KITTI dataset, results show that the direct perception approach can generalize well to real driving images.
]]></description>
<dc:subject>driverless-cars robotics sensoria feature-construction data-fusion algorithms rather-interesting image-processing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b14358615b9c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:driverless-cars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensoria"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.05921">
    <title>[1505.05921] Identifying Modes of Intent from Driver Behaviors in Dynamic Environments</title>
    <dc:date>2015-09-12T13:23:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.05921</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi- and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.
]]></description>
<dc:subject>autonomous-cars robotics inference feature-extraction prediction nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f1ae6c7a19ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autonomous-cars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.07168">
    <title>[1505.07168] Getting Close Without Touching: Near-Gathering for Autonomous Mobile Robots</title>
    <dc:date>2015-09-12T13:09:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.07168</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we study the Near-Gathering problem for a finite set of dimensionless, deterministic, asynchronous, anonymous, oblivious and autonomous mobile robots with limited visibility moving in the Euclidean plane in Look-Compute-Move (LCM) cycles. In this problem, the robots have to get close enough to each other, so that every robot can see all the others, without touching (i.e., colliding with) any other robot. The importance of solving the Near-Gathering problem is that it makes it possible to overcome the restriction of having robots with limited visibility. Hence it allows to exploit all the studies (the majority, actually) done on this topic in the unlimited visibility setting. Indeed, after the robots get close enough to each other, they are able to see all the robots in the system, a scenario that is similar to the one where the robots have unlimited visibility. 
We present the first (deterministic) algorithm for the Near-Gathering problem, to the best of our knowledge, which allows a set of autonomous mobile robots to nearly gather within finite time without ever colliding. Our algorithm assumes some reasonable conditions on the input configuration (the Near-Gathering problem is easily seen to be unsolvable in general). Further, all the robots are assumed to have a compass (hence they agree on the "North" direction), but they do not necessarily have the same handedness (hence they may disagree on the clockwise direction). 
We also show how the robots can detect termination, i.e., detect when the Near-Gathering problem has been solved. This is crucial when the robots have to perform a generic task after having nearly gathered. We show that termination detection can be obtained even if the total number of robots is unknown to the robots themselves (i.e., it is not a parameter of the algorithm), and robots have no way to explicitly communicate.
]]></description>
<dc:subject>robotics collective-intelligence swarms planning algorithms pattern-formation optimization nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5e4aa6afc28a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pattern-formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.6067">
    <title>[1411.6067] Viewpoints and Keypoints</title>
    <dc:date>2015-08-29T12:43:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.6067</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We characterize the problem of pose estimation for rigid objects in terms of determining viewpoint to explain coarse pose and keypoint prediction to capture the finer details. We address both these tasks in two different settings - the constrained setting with known bounding boxes and the more challenging detection setting where the aim is to simultaneously detect and correctly estimate pose of objects. We present Convolutional Neural Network based architectures for these and demonstrate that leveraging viewpoint estimates can substantially improve local appearance based keypoint predictions. In addition to achieving significant improvements over state-of-the-art in the above tasks, we analyze the error modes and effect of object characteristics on performance to guide future efforts towards this goal.
]]></description>
<dc:subject>image-analysis robotics machine-learning inference rather-interesting nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:08fda1568a1c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.06173">
    <title>[1507.06173] Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo</title>
    <dc:date>2015-08-09T14:56:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.06173</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. 
To address this challenge we propose a generative probabilistic model that accurately relates latent imaging conditions to observed camera responses. While principled, realtime inference in the model turns out to be infeasible, and we propose to employ efficient non-parametric regression trees to approximate the model outputs. As a result we are able to provide, for each pixel, at video frame rate, estimates and uncertainty for depth, effective albedo, and ambient light intensity. These results we present are state-of-the-art in depth imaging. 
The flexibility of our approach allows us to easily enrich our generative model. We demonstrate that by extending the original single-path model to a two-path model, capable of describing some multipath effects. The new model is seamlessly integrated in the system at no additional computational cost. 
Our work also addresses the important question of optimal exposure design in pulsed TOF systems. Finally, for benchmark purposes and to obtain realistic empirical priors of multipath and insights into this phenomena, we propose a physically accurate simulation of multipath phenomena.
]]></description>
<dc:subject>image-analysis robotics rather-interesting nudge-targets machine-learning inference 3d</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:70d35e5c1b8d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biorxiv.org/content/early/2015/03/19/016725">
    <title>Self-organization of Computation in Neural Systems | bioRxiv</title>
    <dc:date>2015-07-31T12:15:58+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2015/03/19/016725</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a cell assembly network with multiple, simultaneously active, and computationally powerful assemblies is formed; a process which is so far not understood. Here we show that the combination of synaptic plasticity with the slower process of synaptic scaling achieves formation of such assembly networks. This type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and - for execution - must cooperate with each other without interference. This mechanism, thus, permits for the first time the guided self-organization of computationally powerful sub-structures in dynamic networks for behavior control.

]]></description>
<dc:subject>self-organization neural-networks robotics rather-interesting artificial-life engineering-design nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3f1ff05d2ca5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.02347">
    <title>[1507.02347] Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination</title>
    <dc:date>2015-07-31T11:45:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.02347</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN). The proposed model is built on coupling of a dynamic vision network, a motor generation network, and a higher level network allocated on top of these two. The simulation experiments using the iCub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures. The results showed that synergetic coordination can be developed via iterative learning through the whole network when spatio-temporal hierarchy and temporal one can be self-organized in the visual pathway and in the motor pathway, respectively, such that the higher level can manipulate them with abstraction.
]]></description>
<dc:subject>robotics deep-learning artificial-intelligence image-processing rather-interesting simulation nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:dcc2f4948f9a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.2197">
    <title>[1403.2197] Compliant Pressure Actuated Cellular Structures</title>
    <dc:date>2015-07-25T11:30:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.2197</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A novel concept for pressure actuated cellular structures that can change their shape between any two given one-dimensional functions was published by Pagitz et al 2012 Bioinspir. Biomim. 7. However, the underlying computational framework is limited to two cell rows and assumes frictionless cell corner hinges. This article extends previous work by taking into account an arbitrary number of cell rows, hinge eccentricities and rotational prings at hinges. Hence it is possible to design compliant pressure actuated cellular structures that can change their shape between any given set of one-dimensional functions. Furthermore, a potential based optimization approach is introduced that reduces the number of iterations by an order of magnitude.
]]></description>
<dc:subject>biological-engineering engineering-design kinematics rather-interesting robotics nanotechnology nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:47fb36e8cc87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1504.06631">
    <title>[1504.06631] Motion Planning for Multi-Link Robots by Implicit Configuration-Space Tiling</title>
    <dc:date>2015-07-20T12:05:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1504.06631</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the problem of motion-planning for free-flying multi-link robots and develop a sampling-based algorithm that is specifically tailored for this type of robots. Our work is based on the simple observation that the set of configurations for which the robot is self-collision free is independent of the obstacles or of the exact placement of the robot. This allows us to eliminate the need to perform costly self-collision checks online when solving motion-planning problems, assuming some offline preprocessing. In particular, given a specific robot our algorithm precomputes a tiling roadmap, which efficiently and implicitly encodes the self-collision free (sub-)space over the entire configuration space, where the latter can be infinite for that matter. To answer any query, in any given scenario, we traverse the tiling roadmap while only testing for collisions with obstacles. Our algorithm suggests more flexibility than the prevailing paradigm in which a precomputed roadmap depends both on the robot and on the scenario at hand. We show through various simulations the effectiveness of this approach. Specifically, in certain settings our algorithm is ten times faster than the RRT algorithm.
]]></description>
<dc:subject>robotics planning constraint-satisfaction machine-learning algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:61fc1c2faeda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://vimeo.com/131823232">
    <title>AADRL Spyropoulos Design Lab on Vimeo</title>
    <dc:date>2015-06-27T14:32:14+00:00</dc:date>
    <link>https://vimeo.com/131823232</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Research from the AADRL Spyropoulos Design Lab exploring an architecture that is self-aware, self-structured and self-assembles. The research explores high population of mobility agents that evolve an architecture that moves beyond the fixed and finite towards a behavioural model of interactive human and machine ecologies."]]></description>
<dc:subject>theodorespyropoulos 2015 architecture self-assembling self-aware robotics video via:robertogreco self-organization emergent-design biological-engineering</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7ee083454fda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theodorespyropoulos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:2015"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-aware"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:robertogreco"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1506.01732">
    <title>[1506.01732] Monocular SLAM Supported Object Recognition</title>
    <dc:date>2015-06-14T12:04:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.01732</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.
]]></description>
<dc:subject>image-analysis algorithms robotics data-fusion machine-learning rather-interesting nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9d7a09ebe738/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1504.02437">
    <title>[1504.02437] Predicting Complete 3D Models of Indoor Scenes</title>
    <dc:date>2015-05-06T10:29:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1504.02437</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of walls, which must conform to a Manhattan structure but is otherwise flexible, and the layout and extent of objects, modeled with CAD-like 3D shapes. We represent both the visible and occluded portions of the scene, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scene. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and overall consistency. We demonstrate encouraging results on the NYU v2 dataset and highlight a variety of interesting directions for future work.
]]></description>
<dc:subject>image-processing rather-interesting 3d inference machine-learning robotics nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:72fec046225f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1504.01716">
    <title>[1504.01716] An Empirical Evaluation of Deep Learning on Highway Driving</title>
    <dc:date>2015-04-19T11:50:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1504.01716</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.
]]></description>
<dc:subject>deep-learning neural-networks robotics nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:68fd51cc326b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.8500">
    <title>[1412.8500] Implementation of a Hierarchical fuzzy controller for a biped robot</title>
    <dc:date>2015-04-09T11:51:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.8500</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper the design of a control system for a biped robot is described. Control is specified for a walk cycle of the robot. The implementation of the control system was done on Matlab Simulink. In this paper a hierarchical fuzzy logic controller (HFLC) is proposed to control a planar biped walk. The HFLC design is bio-inspired from human locomotion system. The proposed method is applied to control five links planar biped into free area and without obstacles.
]]></description>
<dc:subject>fuzzy-systems robotics control-theory engineering-design nudge-targets multiobjective-optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3e8f3d94dd3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fuzzy-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.4847">
    <title>[1412.4847] A representation of robotic behaviors using component port arbitration</title>
    <dc:date>2015-03-15T12:45:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.4847</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Developing applications considering reactiveness, scalability and re-usability has always been at the center of attention of robotic researchers. Behavior-based architectures have been proposed as a programming paradigm to develop robust and complex behaviors as integration of simpler modules whose activities are directly modulated by sensory feedback or input from other models. The design of behavior based systems, however, becomes increasingly difficult as the complexity of the application grows. This article proposes an approach for modeling and coordinating behaviors in distributed architectures based on port arbitration which clearly separates representation of the behaviors from the composition of the software components. Therefore, based on different behavioral descriptions, the same software components can be reused to implement different applications.
]]></description>
<dc:subject>robotics behavior-driven-design representation rather-interesting agent-based ontology nudge-targets problem-decomposition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:42d8fd3dad7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:behavior-driven-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ontology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-decomposition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1503.00237">
    <title>[1503.00237] Task Allocation in Robotic Swarms: Explicit Communication Based Approaches</title>
    <dc:date>2015-03-03T11:19:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1503.00237</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we study multi robot cooperative task allocation issue in a situation where a swarm of robots is deployed in a confi?ned unknown environment where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should cover this spots as far as possible to do cleaning and sampling actions desirably. It means that they should discover the spots cooperatively and spread proportional to the spots area and avoid from remaining idle. We proposed 4 self-organized distributed methods which are called hybrid methods for coping with this scenario. In two diffe?rent experiments the performance of the methods is analyzed. We compared them with each other and investigated their scalability and robustness in term of single point of failure.
]]></description>
<dc:subject>collective-intelligence robotics planning collaboration emergent-design nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:eea0b70d1788/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lanl.arxiv.org/abs/1412.5711">
    <title>[1412.5711] Simulation leagues: Enabling replicable and robust investigation of complex robotic systems</title>
    <dc:date>2014-12-28T14:04:20+00:00</dc:date>
    <link>http://lanl.arxiv.org/abs/1412.5711</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Physically-realistic simulated environments are powerful platforms for enabling measurable, replicable and statistically-robust investigation of complex robotic systems. Such environments are epitomised by the RoboCup simulation leagues, which have been successfully utilised to conduct massively-parallel experiments in topics including: optimisation of bipedal locomotion, self-localisation from noisy perception data and planning complex multi-agent strategies without direct agent-to-agent communication. Many of these systems are later transferred to physical robots, making the simulation leagues invaluable well-beyond the scope of simulated soccer matches. In this study, we provide an overview of the RoboCup simulation leagues and describe their properties as they pertain to replicable and robust robotics research. To demonstrate their utility directly, we leverage the ability to run parallelised experiments to evaluate different competition formats (e.g. round robin) for the RoboCup 2D simulation league. Our results demonstrate that a previously-proposed hybrid format minimises fluctuations from 'true' (statistically-significant) team performance rankings within the time constraints of the RoboCup world finals. Our experimental analysis would be impossible with physical robots alone, and we encourage other researchers to explore the potential for enriching their experimental pipelines with simulated components, both to minimise experimental costsand enable others to replicate and expand upon their results in a hardware-independent manner.
]]></description>
<dc:subject>simulation testing robotics acceptance-testing standards-setting replicability engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0ea0bc87f7fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:acceptance-testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:standards-setting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:replicability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1404.5828">
    <title>[1404.5828] Motion planning and Collision Avoidance using Non-Gradient Vector Fields. Technical Report</title>
    <dc:date>2014-12-26T19:48:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.5828</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper presents a novel feedback method on the motion planning for unicycle robots in environments with static obstacles, along with an extension to the distributed planning and coordination in multi-robot systems. The method employs a family of 2-dimensional analytic vector fields, whose integral curves exhibit various patterns depending on the value of a parameter lambda. More specifically, for an a priori known value of lambda, the vector field has a unique singular point of dipole type and can be used to steer the unicycle to a goal configuration. Furthermore, for the unique value of lambda that the vector field has a continuum of singular points, the integral curves are used to define flows around obstacles. An almost global feedback motion plan can then be constructed by suitably blending attractive and repulsive vector fields in a static obstacle environment. The method does not suffer from the appearance of sinks (stable nodes) away from goal point. Compared to other similar methods which are free of local minima, the proposed approach does not require any parameter tuning to render the desired convergence properties. The paper also addresses the extension of the method to the distributed coordination and control of multiple robots, where each robot needs to navigate to a goal configuration while avoiding collisions with the remaining robots, and while using local information only. More specifically, based on the results which apply to the single-robot case, a motion coordination protocol is presented which guarantees the safety of the multi-robot system and the almost global convergence of the robots to their goal configurations. The efficacy of the proposed methodology is demonstrated via simulation results in static and dynamic environments.
]]></description>
<dc:subject>planning robotics representation nudge-targets algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b816070d6135/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.3827">
    <title>[1401.3827] Efficient Planning under Uncertainty with Macro-actions</title>
    <dc:date>2014-12-25T11:20:05+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.3827</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions. We present a formal analysis of our approach, and examine its performance on two very large simulation experiments: scientific exploration and a target monitoring domain. We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multi-step lookahead is required to achieve good performance.
]]></description>
<dc:subject>planning algorithms summarization the-mangle-in-practice robotics nudge-targets representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:679639a449ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:summarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.4612">
    <title>[1401.4612] Modelling Observation Correlations for Active Exploration and Robust Object Detection</title>
    <dc:date>2014-12-24T13:37:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.4612</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, real-world environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from a human collaborator referring to objects of interest; the robot must be able to accurately detect these objects to correctly understand the instructions. However, existing object detection, while competent, is not perfect. In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene. This paper presents an online planning algorithm which learns an explicit model of the spatial dependence of object detection and generates plans which maximize the expected performance of the detection, and by extension the overall plan performance. Crucially, the learned sensor model incorporates spatial correlations between measurements, capturing the fact that successive measurements taken at the same or nearby locations are not independent. We show how this sensor model can be incorporated into an efficient forward search algorithm in the information space of detected objects, allowing the robot to generate motion plans efficiently. We investigate the performance of our approach by addressing the tasks of door and text detection in indoor environments and demonstrate significant improvement in detection performance during task execution over alternative methods in simulated and real robot experiments.
]]></description>
<dc:subject>planning robotics isn't-this-just-=ghost-planning? algorithms to-review system-of-professions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d1e5753629be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:isn't-this-just-=ghost-planning?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-review"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.7267">
    <title>[1411.7267] Behaviour Trees for Evolutionary Robotics</title>
    <dc:date>2014-12-18T12:16:15+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.7267</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the learnt behaviour in simulation 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.
]]></description>
<dc:subject>evolutionary-algorithms robotics online-learning dreaming planning nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3f0e05e1d1b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dreaming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.6837">
    <title>[1411.6837] A Flexible and Robust Large Scale Capacitive Tactile System for Robots</title>
    <dc:date>2014-12-14T14:51:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.6837</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Capacitive technology allows building sensors that are small, compact and have high sensitivity. For this reason it has been widely adopted in robotics. In a previous work we presented a compliant skin system based on capacitive technology consisting of triangular modules interconnected to form a system of sensors that can be deployed on non-flat surfaces. This solution has been successfully adopted to cover various humanoid robots. The main limitation of this and all the approaches based on capacitive technology is that they require to embed a deformable dielectric layer (usually made using an elastomer) covered by a conductive layer. This complicates the production process considerably, introduces hysteresis and limits the durability of the sensors due to ageing and mechanical stress. 
In this paper we describe a novel solution in which the dielectric is made using a thin layer of 3D fabric which is glued to conductive and protective layers using techniques adopted in the clothing industry. As such, the sensor is easier to produce and has better mechanical properties. Furthermore, the sensor proposed in this paper embeds transducers for thermal compensation of the pressure measurements. We report experimental analysis that demonstrates that the sensor has good properties in terms of sensitivity and resolution. Remarkably we show that the sensor has very low hysteresis and effectively allows compensating drifts due to temperature variations.
]]></description>
<dc:subject>metamaterials materials-science engineering-design robotics rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5628e26581e7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metamaterials"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1407.8194">
    <title>[1407.8194] Fence patrolling by mobile agents with distinct speeds</title>
    <dc:date>2014-12-08T21:57:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1407.8194</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Suppose we want to patrol a fence (line segment) using k mobile agents with given speeds v_1, ..., v_k so that every point on the fence is visited by an agent at least once in every unit time period. Czyzowicz et al. conjectured that the maximum length of the fence that can be patrolled is (v_1 + ... + v_k)/2, which is achieved by the simple strategy where each agent i moves back and forth in a segment of length v_i/2. We disprove this conjecture by a counterexample involving k = 6 agents. We also show that the conjecture is true for k = 2, 3.
]]></description>
<dc:subject>collective-intelligence planning operations-research robotics rather-interesting nudge-targets mathematical-recreations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:601b3a5ba546/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.4426">
    <title>[1410.4426] Partial Force Control of Constrained Floating-Base Robots</title>
    <dc:date>2014-11-28T21:40:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.4426</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Legged robots are typically in rigid contact with the environment at multiple locations, which add a degree of complexity to their control. We present a method to control the motion and a subset of the contact forces of a floating-base robot. We derive a new formulation of the lexicographic optimization problem typically arising in multitask motion/force control frameworks. The structure of the constraints of the problem (i.e. the dynamics of the robot) allows us to find a sparse analytical solution. This leads to an equivalent optimization with reduced computational complexity, comparable to inverse-dynamics based approaches. At the same time, our method preserves the flexibility of optimization based control frameworks. Simulations were carried out to achieve different multi-contact behaviors on a 23-degree-offreedom humanoid robot, validating the presented approach. A comparison with another state-of-the-art control technique with similar computational complexity shows the benefits of our controller, which can eliminate force/torque discontinuities.
]]></description>
<dc:subject>robotics kinematics simulation engineering-design nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:37e221c82b4d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.7434">
    <title>[1311.7434] Observability, Identifiability and Sensitivity of Vision-Aided Navigation</title>
    <dc:date>2014-11-16T11:40:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.7434</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We analyze the observability of motion estimates from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed identification/filtering, and the resulting observability analysis provides a necessary condition for any algorithm to converge to a unique point estimate. Unfortunately, most models treat sensor bias rates as noise, independent of other states including biases themselves, an assumption that is patently violated in practice. When this assumption is lifted, the resulting model is not observable, and therefore past analyses cannot be used to conclude that the set of states that are indistinguishable from the measurements is a singleton. In other words, the resulting model is not observable. We therefore re-cast the analysis as one of sensitivity: Rather than attempting to prove that the indistinguishable set is a singleton, which is not the case, we derive bounds on its volume, as a function of characteristics of the input and its sufficient excitation. This provides an explicit characterization of the indistinguishable set that can be used for analysis and validation purposes.
]]></description>
<dc:subject>robotics algorithms formalization the-mangle-in-practice sensor-integration learning-by-doing how-very-odd nudge-targets performance-measure who-exactly-is-observability-for?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4736fb2501ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensor-integration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:how-very-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:who-exactly-is-observability-for?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.3601">
    <title>[1310.3601] Flocking algorithm for autonomous flying robots</title>
    <dc:date>2014-10-16T12:41:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.3601</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in the control algorithm of the robots. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour of robots requires the thorough and realistic modeling of the robot and the environment as well. In this paper, first, we present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of the communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results about the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters.
]]></description>
<dc:subject>flocking robotics swarms boids algorithms emergent-design experiment rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ed8aaf98a53a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:flocking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boids"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.6070">
    <title>[1401.6070] On Fence Patrolling by Mobile Agents</title>
    <dc:date>2014-10-16T12:32:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.6070</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Suppose that a fence needs to be protected (perpetually) by k mobile agents with maximum speeds v1,…,vk so that no point on the fence is left unattended for more than a given amount of time. The problem is to determine if this requirement can be met, and if so, to design a suitable patrolling schedule for the agents. Alternatively, one would like to find a schedule that minimizes the \emph{idle time}, that is, the longest time interval during which some point is not visited by any agent. We revisit this problem, introduced by Czyzowicz et al.(2011), and discuss several strategies for the cases where the fence is an open and a closed curve, respectively. 
In particular: (i) we disprove a conjecture by Czyzowicz et al. regarding the optimality of their Algorithm 2 for unidirectional patrolling of a closed fence; (ii) we present an algorithm with a lower idle time for patrolling an open fence, improving an earlier result of Kawamura and Kobayashi.
]]></description>
<dc:subject>robotics collective-intelligence toy-problems nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:859c0e7a6c32/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:toy-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.7525">
    <title>[1406.7525] Fusion Based Holistic Road Scene Understanding</title>
    <dc:date>2014-09-28T11:55:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7525</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, we propose an approach that jointly tackles object-level image segmentation and semantic region labeling within a conditional random field (CRF) framework. Specifically, we first generate semantic object hypotheses by clustering 3D points, learning their prior appearance models, and using a deep learning method for reasoning their semantic categories. The learned priors, together with spatial and geometric contexts, are incorporated in CRF. With this formulation, visual and range data are fused thoroughly, and moreover, the coupled segmentation and semantic labeling problem can be inferred via Graph Cuts. Our approach is validated on the challenging KITTI dataset that contains diverse complicated road scenarios. Both quantitative and qualitative evaluations demonstrate its effectiveness.
]]></description>
<dc:subject>data-fusion machine-learning image-processing robotics algorithms nudge-targets performance-measure define-understanding-please</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:28128d3a88d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-fusion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-understanding-please"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.3083">
    <title>[1403.3083] A Novel Method to Extract Rocks from Mars Images</title>
    <dc:date>2014-08-20T10:07:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.3083</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, a novel method is proposed to extract rocks from Martian surface images by using 8 data field. It models the interaction between two pixels of an image in the context of imagery 9 characteristics. First, foreground rocks are differed from background information by binarizing 10 image on roughly partitioned images. Second, foreground rocks are grouped into clusters by 11 locating the centers and edges of clusters in data field via hierarchical grids. Third, the target 12 rocks are discovered for the Mars Exploration Rover (MER) to keep healthy paths. The 13 experiment with images taken by MER shows the proposed method is practical and potential.
]]></description>
<dc:subject>image-processing image-segmentation algorithms robotics nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7ec1cab451f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.4599">
    <title>[1401.4599] Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation</title>
    <dc:date>2014-02-22T12:45:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.4599</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPlaces are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. 
When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPlace, and bases its decisions on this ARPlace, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPlaces in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPlaces leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot.
]]></description>
<dc:subject>planning robotics algorithms frameworks-frameworks-frameworks nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:22d49e3e4bd8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:frameworks-frameworks-frameworks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.2460">
    <title>[1311.2460] Vision-Guided Robot Hearing</title>
    <dc:date>2014-01-23T13:23:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.2460</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Natural human-robot interaction in complex and unpredictable environments is one of the main research lines in robotics. In typical real-world scenarios, humans are at some distance from the robot and the acquired signals are strongly impaired by noise, reverberations and other interfering sources. In this context, the detection and localisation of speakers plays a key role since it is the pillar on which several tasks (e.g.: speech recognition and speaker tracking) rely. We address the problem of how to detect and localize people that are both seen and heard by a humanoid robot. We introduce a hybrid deterministic/probabilistic model. Indeed, the deterministic component allows us to map the visual information into the auditory space. By means of the probabilistic component, the visual features guide the grouping of the auditory features in order to form AV objects. The proposed model and the associated algorithm are implemented in real-time (17 FPS) using a stereoscopic camera pair and two microphones embedded into the head of the humanoid robot NAO. We performed experiments on (i) synthetic data, (ii) a publicly available data set and (iii) data acquired using the robot. The results we obtained validate the approach and encourage us to further investigate how vision can help robot hearing.
]]></description>
<dc:subject>robotics sound-recognition hearing algorithms synthesis multimodal-sensing modeling nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0dbb9b97ad10/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sound-recognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hearing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multimodal-sensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>