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检索条件"机构=the Learning Algorithms and Systems Laboratory"

共 81 条记录，以下是21-30 订阅

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A Probabilistic Programming by Demonstration Framework Handling Constraints in Joint Space and Task Space

A Probabilistic Programming by Demonstration Framework Handl...

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS 2008), vol.1

作者： Sylvain Calinon Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. In previous work, we proposed an approach based on Gaussian Mixture Regression (GMR) to find a controller for the robot reproducing the essential characteristics of a skill in joint space and in task space through Lagrange optimization. In this paper, we extend this approach to a more generic procedure handling simultaneously constraints in joint space and in task space by combining directly the probabilistic representation of the task constraints with a simple Jacobian-based inverse kinematics solution. Experiments with two 5-DOFs Katana robots are presented with manipulation tasks that consist of handling and displacing a set of objects.

关键词： Joints Aerospace electronics Robots Trajectory Glass Kinematics Jacobian matrices

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Dynamical System Approach to Navigation Around Obstacles

Dynamical System Approach to Navigation Around Obstacles

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European Control Conference (ECC)

作者： Aradhana Nayak Aude Billard Learning algorithms and systems laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this article, we propose a dynamical system to avoid obstacles which are star shaped and simultaneously converge to a goal. The convergence is almost-global in a domain and the stationary points are identified explicitly. Our approach is based on the idea that an ideal vector field which avoids the obstacle traverses its boundary up to when a clear path to the goal is available. We show the existence of this clear path through a set connecting the boundary of the obstacle and the goal. The traversing vector field is determined for an arbitrary obstacle (de-scribed by a set of points) by separating it into cluster of stars. We propose an algorithm which is linear in number of points inside the obstacle. We verify the theoretical results presented with various hand drawn obstacle sets. Our methodology is also extended to obstacles which are not star-shaped, and, those which exist in high dimensions.

关键词： Support vector machines Navigation Shape Level set Heuristic algorithms Stars Europe

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learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators

Learning nonlinear multi-variate motion dynamics for real-ti...

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IEEE-RAS International Conference on Humanoid Robots

作者： E. Gribovskaya Aude Billard Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781424445875

We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multi-variate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: (i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; (ii) it allows to generalize a motion to unseen context; (iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.

关键词： Manipulator dynamics Position control Robot kinematics Humanoid robots Motion control Humans Control systems Encoding Dynamic programming Robot programming

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Using reinforcement learning to adapt an imitation task

Using reinforcement learning to adapt an imitation task

引用

2007 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Florent Guenter Aude G. Billard Learning Algorithms and Systems Laboratory School of Engineering Ecole Polytechnique Federale de Lausanne Switzerland

The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots that can be accomplished by everyone. When a demonstrator teaches a task to a robot, he/she shows some ways of fulfilling the task, but not all the possibilities. The robot must then be able to reproduce the task even when unexpected perturbations occur. In this case, it has to learn a new solution. In this paper, we describe a system that allows a robot to re-learn constrained reaching tasks by combining the knowledge acquired during the demonstration, with that acquired though reinforcement learning.

关键词： learning Robot programming Intelligent robots Educational robots Humanoid robots USA Councils Pressing Human robot interaction Animals

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learning from failure

Learning from failure

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

作者： Daniel H Grollman Aude G Billard Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

In the canonical Robot learning from Demonstration scenario a robot observes performances of a task and then develops an autonomous controller. Current work acknowledges that humans may be suboptimal demonstrators and refines the controller for improved performance. However, there is still an assumption that the demonstrations are successful examples of the task. We here consider the possibility that the human has failed, and propose a model to minimize the possibility of the robot making the same mistakes.

关键词： Abstracts Robots

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Synthesizing robotic handwriting motion by learning from human demonstrations 25

Synthesizing robotic handwriting motion by learning from hum...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Yin, Hang Alves-Oliveira, Patricia Melo, Francisco S. Billard, Aude Paiva, Ana GAIPS INESC-ID and Instituto Superior Técnico Universidade de Lisboa Portugal Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland CIS-IUL INESC-ID Lisboa Portugal

This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the robot to learn themselves the handwriting skill. The framework adapts the principle behind ensemble methods - where improved performance is obtained by combining the output of multiple simple algorithms - in an inverse optimal control problem. This integration addresses the challenges of rapid extraction and representation of multiple-mode motion trajectories, with the cost forms which are transferable and interpretable in the development of the robot compliance control. It also introduces the incorporation of a human movement inspired feature, which provides intuitive motion modulation to generalize the synthesis with poor robotic written samples for children to identify and correct. We present the results on the success of synthesizing a variety of natural-looking motion samples based upon the learned cost functions. The framework is validated by a user study, where the synthesized dynamical motion is shown to be hard to distinguish from the real human handwriting.

关键词： Cost functions

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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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Active Teaching in Robot Programming by Demonstration

Active Teaching in Robot Programming by Demonstration

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sylvain Calinon Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Robot programming by demonstration (RbD) covers methods by which a robot learns new skills through human guidance. In this work, we take the perspective that the role of the teacher is more important than just being a model of successful behaviour, and present a probabilistic framework for RbD which allows to extract incrementally the essential characteristics of a task described at a trajectory level. To demonstrate the feasibility of our approach, we present two experiments where manipulation skills are transferred to a humanoid robot by means of active teaching methods that put the human teacher in the loop of the robot's learning. The robot first observes the task performed by the user (through motion sensors) and the robot's skill is then refined progressively by embodying the robot and putting it through the motion (kinesthetic teaching).

关键词： Education Robot programming Educational robots Robot sensing systems Human robot interaction Humanoid robots Biological system modeling Laboratories Joints Problem-solving

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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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Tactile guidance for policy refinement and reuse

Tactile guidance for policy refinement and reuse

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IEEE International Conference on Development and learning, ICDL

作者： Brenna D. Argall Eric L. Sauser Aude G. Billard Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations can degrade policy performance. This work presents an approach for policy improvement and adaptation through a tactile interface located on the body of a robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies. We validate TPC on a humanoid robot performing grasp-positioning tasks. The performance of the demonstrated policy is found to improve with tactile corrections. Tactile guidance also is shown to enable the development of policies able to successfully execute novel, undemonstrated, tasks.

关键词： Humans Robot kinematics Manipulators Tactile sensors Joints

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