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

共 35 条记录，以下是1-10 订阅

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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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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 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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learning Dynamical System Modulation for Constrained Reaching Tasks

Learning Dynamical System Modulation for Constrained Reachin...

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IEEE-RAS International Conference on Humanoid Robots

作者： Micha Hersch Florent Guenter Sylvain Calinon Aude G. Billard Learning Algorithms and Systems Laboratory-LASA School of Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)1424401992

In this paper we combine kinesthetic demonstrations and dynamical systems to enable a humanoid robot to imitate constrained reaching gestures directed toward a target. Using a learning algorithm based on Gaussian mixture regression, the task constraints are extracted from several demonstrations. Those constraints take the form of desired velocity profiles for the end-effector and joint angle variables, with associated covariance matrices describing the variations allowed around the desired profiles. Those constraints are then used to modulate a dynamical system which has the reaching target as attractor. This way, the reaching trajectory can be reshaped in order to satisfy the constraints of the task, while preserving the adaptability and robustness provided by the dynamical system. In particular, the system can adapt to changes in the initial conditions and to target displacements occurring during the movement execution. We first evaluate the potential of this method on experiments involving the Hoap3 humanoid robot putting an object into a box. We then show how a manipulation tasks can be executed as sequences of such constrained reaching movement. This is illustrated on a packaging task performed by the robot

关键词： Humanoid robots Service robots Humans Robot control Laboratories Covariance matrix Robustness Packaging Electrical equipment industry Robot programming

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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 of Grasp Adaptation through Experience and Tactile Sensing

Learning of Grasp Adaptation through Experience and Tactile ...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Miao Li Yasemin Bekiroglu Danica Kragic Aude Billard Learning Algorithms and Systems Laboratory (LASA) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzeland KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479969357

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

关键词： Uncertainty Training Stability analysis Impedance Grasping Estimation Support vector machines

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Motion learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans

Motion Learning and Adaptive Impedance for Robot Control dur...

引用

2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Elena Gribovskaya Aude Billard Abderrahmane Kheddar Learning Algorithms and Systems Laboratory (LASA) EPFLSwitzerland CNRS-UM2 LIRMM MontpellierFranceand the CNRS-AIST JRLUMI3218/CRTJapan

This article combines programming by demon- stration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. learning a task model allows the robot to anticipate the partner’s intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compen- sate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm that tunes the impedance parameters, so as to ensure accurate reproduction. To facilitate the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained with simulation of a dyad of two planar 2-DOF robots.

关键词： Impedance Force Adaptation models Robot kinematics Kinematics Humans

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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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learning complex sequential tasks from demonstration: A pizza dough rolling case study 16

Learning complex sequential tasks from demonstration: A pizz...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Nadia Figueroa Ana Lucia Pais Ureche Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland Ecole Polytechnique Federale de Lausanne Lausanne VD CH

ISBN: (纸本)9781467383707

This paper introduces a hierarchical framework that is capable of learning complex sequential tasks from human demonstrations through kinesthetic teaching, with minimal human intervention. Via an automatic task segmentation and action primitive discovery algorithm, we are able to learn both the high-level task decomposition (into action primitives), as well as low-level motion parameterizations for each action, in a fully integrated framework. In order to reach the desired task goal, we encode a task metric based on the evolution of the manipulated object during demonstration, and use it to sequence and parametrize each action primitive. We illustrate this framework with a pizza dough rolling task and show how the learned hierarchical knowledge is directly used for autonomous robot execution.

关键词： Hidden Markov models Motion segmentation Force Measurement Shape Education

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