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Publication Details

Reference TypeConference Proceedings
Author(s)Kalakrishnan, M.;Righetti, L.;Pastor, P.;Schaal, S.
TitleLearning force control policies for compliant manipulation
Journal/Conference/Book TitleIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Keywordsmovement primitives, reinforcement learning, PI2, skill learning, force control
AbstractDeveloping robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manip-ulation tasks imply complex contact interactions with the external world, and in-volve reasoning about the forces and torques to be applied. Planning under con-tact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learn-ing. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque pro-files by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.
Place PublishedSept. 25-30, San Francisco, CA
Short TitleLearning force control policies for compliant manipulation

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