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

共 35 条记录，以下是21-30 订阅

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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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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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learning collaborative manipulation tasks by demonstration using a haptic interface

Learning collaborative manipulation tasks by demonstration u...

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International Conference on Advanced Robotics (ICAR)

作者： Sylvain Calinon Paul Evrard Elena Gribovskaya Aude Billard Abderrahmane Kheddar Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland UMI3218/CRT CNRS-AIST JRL Tsukuba Japan

This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. learning extracts statistical redundancies in the examples provided during training by using Gaussian Mixture Regression and Hidden Markov Model. Haptic communication reflects more than pure dynamic information on the task, and includes communication patterns, which result from the two users constantly adapting their hand motion to coordinate in time and space their respective motions. We show that the proposed statistical model can efficiently encapsulate typical communication patterns across different dyads of users, that are stereotypical of collaborative behaviours between humans and robots. The proposed learning approach is generative and can be used to drive the robot's retrieval of the task by ensuring a faithful reproduction of the overall dynamics of the task, namely by reproducing the force patterns for both lift the object and adapt to the human user's hand motion. This work shows the potential that teleoperation holds for transmitting both dynamic and communicative information on the task, which classical methods for programming by demonstration have traditionally overlooked.

关键词： Collaboration Haptic interfaces Robot kinematics Hidden Markov models Humans Orbital robotics Dynamic programming Robot control Communication system control Data mining

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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

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IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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EMG-based learning approach for estimating wrist motion

EMG-based learning approach for estimating wrist motion

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

作者： S. El-Khoury I. Batzianoulis C. W. Antuvan S. Contu L. Masia S. Micera A. Billard Learning Algorithms and Systems Laboratory (LASA)at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Assistive Robotics and Interactive Ergonomic Systems Laboratory (ARIES) at Nanyang Technological University Singapore Translational Neural Engineering Laboratory (TNE) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781424492695

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.

关键词： EMG EMG-based learning approach estimating wrist motion

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Evaluation of an Industrial Robotic Assistant in an Ecological Environment

Evaluation of an Industrial Robotic Assistant in an Ecologic...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Baptiste Busch Giuseppe Cotugno Mahdi Khoramshahi Grigorios Skaltsas Dario Turchi Leonardo Urbano Mirko Wächter You Zhou Tamim Asfour Graham Deacon Duncan Russell Aude Billard EPFL Learning Algorithms and Systems Laboratory (LASA) Lausanne Switzerland Robotics Research Group of Ocado Technology Hatfield UK University of Hertfordshire Hatfield UK KIT High Performance Humanoid Technologies Lab (H2T) Karlsruhe Germany

Social robotic assistants have been widely studied and deployed as telepresence tools or caregivers. Evaluating their design and impact on the people interacting with them is of prime importance. In this research, we evaluate the usability and impact of ARMAR-6, an industrial robotic assistant for maintenance tasks. For this evaluation, we have used a modified System Usability Scale (SUS) to assess the general usability of the robotic system and the Godspeed questionnaire series for the subjective perception of the coworker. We have also recorded the subjects' gaze fixation patterns and analyzed how they differ when working with the robot compared to a human partner.

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The DoF-Box Project: An Educational Kit for Configurable Robots

The DoF-Box Project: An Educational Kit for Configurable Rob...

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2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Pages 805-1210

作者： D. Daidie O. Barbey A. Guignard D. Roussy F. Guenter A. Ijspeert A. Billard Learning Algorithms and Systems Laboratory (LASA) School of engineering Ecole Polytechnique Federale de Lausanne (EPFL) 1015 Lausanne Switzerland

ISBN: (纸本)9781424412631;1424412633

This paper describes the DoF-Box, a modular, reconfigurable robot, used in Hands-on practicals of robotics taught to microengineering students. We present the hardware and software of the robots and describe the pedagogical approach adopted in the practicals. This approach encourages the creativity and imagination of the students, while ensuring that the students put in practice fundamental laws of control they are taught in class. Some of the results, including particular design of robots, obtained by the students are also shown.

关键词： Robot Students Imagination configurability robotics Computer hardware Reconfigurable robot Hand computer software

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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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