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检索条件"机构=Learning Algorithms and Systems Laboratory - LASA"

共 35 条记录，以下是11-20 订阅

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learning cost function and trajectory for robotic writing motion

Learning cost function and trajectory for robotic writing mo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Hang Yin Ana Paiva Aude Billard Intelligent Agents and Synthetic Characters Group (GAIPS) University of Lisbon Portugal Ecole Polytechnique Fédérale de Lausanne (EPFL) Learning Algorithms and Systems Laboratory (LASA) Switzerland

ISBN: (纸本)9781479971756

We present algorithms for inferring the cost function and reference trajectory from human demonstrations of hand-writing tasks. These two key elements are then used, through optimal control, to generate an impedance-based controller for a robotic hand. The key novelty lies in the flexibility of the feature design in the composition of the cost function, in contrast to the traditional approaches that consider linearly combined features. Cross-entropy-based methods form the core of our learning technique, resulting in sample-based stochastic algorithms for task encoding and decoding. The algorithms are validated using an anthropomorphic robot hand. We assess that the correct compliance is well encapsulated by subjecting the robot to perturbations during task reproduction.

关键词： Trajectory Robots Cost function Optimal control Decoding Stochastic processes

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Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ajay Kumar Tanwani Jose del. R. Millan Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Defitech Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuropros-thetics in EPFL

ISBN: (纸本)9781424479276

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.

关键词： Electroencephalography Robots Decoding Trajectory Accuracy Correlation Training

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Transfer in Inverse Reinforcement learning for Multiple Strategies

Transfer in Inverse Reinforcement Learning for Multiple Stra...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Ajay Kumar Tanwani Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Switzerland

ISBN: (纸本)9781467363563

We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. We assume that this variety across experts' demonstration is due to the fact that each expert/strategy is driven by a different reward function, where reward function is expressed as a linear combination of a set of known features. Consequently, we can learn all the expert strategies by forming a convex set of optimal deterministic policies, from which one can match any unseen expert strategy drawn from this set. Instead of learning from scratch every optimal policy in this set, the learner transfers knowledge from the set of learned policies to bootstrap its search for new optimal policy. We demonstrate our approach on a simulated mini-golf task where the 7 degrees of freedom Barrett WAM robot arm learns to sequentially putt on different holes in accordance with the playing strategies of the expert.

关键词： Multiple Strategies WAM playing strategies

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learning a real time grasping strategy

Learning a real time grasping strategy

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bidan Huang Sahar El-Khoury Miao Li Joanna J. Bryson Aude Billard Learning Algorithms and Systems Laboratory (LASA) Swiss Federal Institute of Technology Lausanne (EPFL) Switzerland Computer Science Department University of Bath UK

Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2.8GHz processor.

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learning Coupled Dynamical systems from human demonstration for robotic eye-arm-hand coordination

Learning Coupled Dynamical Systems from human demonstration ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Luka Lukic José Santos-Victor Aude Billard Learning Algorithms and Systems Laboratory (LASA) EPFL Switzerland Computer Vision Laboratory (VISLAB) IST Portugal

Efficient, adaptive and reliable visuomotor control system is crucial to enable robots to display flexibility in the face of changes in the environment. This paper takes inspiration in human eye-arm-hand coordination pattern to develop an equivalently robust robot controller. We recorded gaze, arm, hand, and trunk data from human subjects in reaching and grasping scenarios with/without obstacle in the workspace. An eye-arm-hand controller is developed, based on our extension of Coupled Dynamical systems (CDS). We exploit the time-invariant properties of the CDS to allow fast adaptation to spatial and temporal perturbations during task completion. CDS global stability guarantees that the eye, the arm and the hand will reach the target in retinal, operational and grasp space respectively. When facing perturbations, the system can re-plan its actions almost instantly, without the need for an additional planning module. Coupling profiles for eye-arm and arm-hand systems can be modulated allowing to adjust the behavior of each slave system with respect to control signals flowing from the corresponding master system. We show how the CDS eye-arm-hand control framework can be used to handle the presence of obstacles in the workspace. The eye-arm-hand controller is validated in a series of experiments conducted with the iCub robot.

关键词： Robot kinematics Couplings Heuristic algorithms

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Online learning of varying stiffness through physical human-robot interaction

Online learning of varying stiffness through physical human-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Klas Kronander Aude Billard Learning Algorithms and Systems Laboratory (LASA) School of Engineering Ecole Polytechnique Federale de Lausanne Switzerland

Programming by Demonstration offers an intuitive framework for teaching robots how to perform various tasks without having to preprogram them. It also offers an intuitive way to provide corrections and refine teaching during task execution. Previously, mostly position constraints have been taken into account when teaching tasks from demonstrations. In this work, we tackle the problem of teaching tasks that require or can benefit from varying stiffness. This extension is not trivial, as the teacher needs to have a way of communicating to the robot what stiffness it should use. We propose a method by which the teacher can modulate the stiffness of the robot in any direction through physical interaction. The system is incremental and works online, so that the teacher can instantly feel how the robot learns from the interaction. We validate the proposed approach on two experiments on a 7-Dof Barrett WAM arm.

关键词： Robots Education Impedance Humans Trajectory Covariance matrix Kinematics

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Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics 12

Augmented-SVM: Automatic space partitioning for combining mu...

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Annual Conference on Neural Information Processing systems

作者： Ashwini Shukla Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federate de Lausanne (EPFL) Lausanne Switzerland-1015

ISBN: (纸本)9781627480031

Non-linear dynamical systems (DS) have been used extensively for building generative models of human behavior. Their applications range from modeling brain dynamics to encoding motor commands. Many schemes have been proposed for encoding robot motions using dynamical systems with a single attractor placed at a predefined target in state space. Although these enable the robots to react against sudden perturbations without any re-planning, the motions are always directed towards a single target. In this work, we focus on combining several such DS with distinct attractors, resulting in a multi-stable DS. We show its applicability in reach-to-grasp tasks where the attractors represent several grasping points on the target object. While exploiting multiple attractors provides more flexibility in recovering from unseen perturbations, it also increases the complexity of the underlying learning problem. Here we present the Augmented-SVM (A-SVM) model which inherits region partitioning ability of the well known SVM classifier and is augmented with novel constraints derived from the individual DS. The new constraints modify the original SVM dual whose optimal solution then results in a new class of support vectors (SV). These new SV ensure that the resulting multi-stable DS incurs minimum deviation from the original dynamics and is stable at each of the attractors within a finite region of attraction. We show, via implementations on a simulated 10 degrees of freedom mobile robotic platform, that the model is capable of real-time motion generation and is able to adapt on-the-fly to perturbations.

关键词： Human behavior perturbation Robot kinematics Attractor region of attraction DYNAMICAL systems minimum deviation Nonlinear Dynamics state space Support Vector Network optimal solution Robot Degree of freedom

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Probabilistic depth image registration incorporating nonvisual information

Probabilistic depth image registration incorporating nonvisu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Manuel Wüthrich Peter Pastor Ludovic Righetti Aude Billard Stefan Schaal Faculty of Micro Engineering EPF Lausanne Switzerland Computational Learning and Motor Control Laboratory (CLMC) USC USA Learning Algorithms and Systems Laboratory (LASA) EPF Lausanne Switzerland

In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available.

关键词： Iterative closest point algorithm Cameras Robots Shape Covariance matrix Visualization Approximation algorithms

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Reaching and grasping kitchenware objects

Reaching and grasping kitchenware objects

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IEEE/SICE International Symposium on System Integration

作者： Rui Figueiredo Ashwini Shukla Duarte Aragão Plinio Moreno Alexandre Bernardino José Santos-Victor Aude Billard Faculty of Electrical and Computer Engineering Institute de Sistemas e Robótica Instituto Superior Técnico Lisbon Portugal Learning Algorithms and Systems Laboratory STI-I2S-LASA École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We integrate software components that allow efficient and successful grasping of kitchenware objects. The contributed components include: The object pose detector, the gripper reaching motion and the grasp hypothesis selection. The object pose detector of Drost et. al. [10] is improved, considering rotationally symmetric objects. The reaching motion execution combines two independent dynamical systems: The approach direction system and its tangent space [21]. The coupling provides a robust reaching component that copes with several gripper configurations. The grasp hypothesis selection filters the object poses by considering the table orientation.

关键词： Grasping Computational modeling Grippers Detectors Couplings Trajectory Heuristic algorithms

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learning to control planar hitting motions in a minigolf-like task

Learning to control planar hitting motions in a minigolf-lik...

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2011 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Klas Kronander Mohammad S.M. Khansari-Zadeh Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Switzerland

A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a non-trivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment.

关键词： Robots Training Vectors Ground penetrating radar Training data Trajectory Sports equipment

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