Motion Learning and Adaptive Impedance for Robot Control during Physical Interaction with Humans
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.
Elena Gribovskaya Abderrahmane Kheddar Aude Billard
Learning Algorithms and Systems Laboratory (LASA),EPFL,Switzerland CNRS-UM2 LIRMM,Montpellier,France,and the CNRS-AIST JRL,UMI3218/CRT,Japan
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4326-4332
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）