Site Search  

Resources » Publication Details

Publication Details

Reference TypeConference Proceedings
Author(s)Kalakrishnan, M.; Righetti, L.; Pastor, P.; Schaal, S.
TitleLearning Force Control Policies for Compliant Robotic Manipulation
Journal/Conference/Book TitleInternational Conference on Machine Learning (ICML)
Keywordsmovement primitives, reinforcement learning, pi2, skill learning, force control
AbstractIn this abstract, we present an approach to learning manipulation tasks on compliant robots through re- inforcement learning. We demonstrate our approach on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table (Fig. 1). We show that our approach can learn the force control policies required to achieve both tasks successfully. The contributions of this work are two-fold: (1) we demonstrate that learning force con- trol policies enables compliant execution of manipu- lation tasks with increased robustness as opposed to stiff position control, and (2) we introduce a policy parameterization that uses finely discretized trajectories coupled with a cost function that ensures smoothness during exploration and learning.

Designed by: Nerses Ohanyan & Jan Peters
Page last modified on June 20, 2013, at 07:00 PM