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Learning human compliant behavior from demonstration for force-based robot manipulation

Robot manipulation is one of prerequisites capability for service robot. However, autonomous manipulation remains a challenging problem for robot to implement the task, where robot has physical interactions and mechanical contacts with its environment. To date, learning from demonstration (LFD) has been successfully applied to enable robot to acquire new manipulation skill. Researches on LFD mainly focus on representing movement trajectory from demonstration and then transferring the new reproduced trajectory to robot. In this paper, a learning framework is introduced to learn compliant behavior from human demonstration and transferring it to robot. Multivariable, position and interaction force, will be simultaneously encoded in a probability model. The control mode of each axis in C-frame is estimated to decouple the position and force control. And an external DMP is presented to reproduce the new desired position or force for new situations. Furthermore, the compliant parameters are analyzed and estimated by combining probability modeling approach and dynamic system approach. After human compliant behavior learning, a hybrid external position/force control is presented to enable robot to produce a human-like compliant behavior. Experiments are performed to demonstrate the effectiveness of the presented learning framework.