A Novel Robot Skill Learning Framework Based on Bilateral Teleoperation

Abstract

In this paper, a bilateral teleoperation-based robot skill learning framework is developed to transfer multi-step and contact manipulation skills from humans to robots. Robot skill acquisition via bilateral teleoperation provides a solution for human teacher to transfer the manipulation skills to robots in a remotely feasible manner. Besides, the bilateral teleoperation with force feedback allows humans in the loop to monitor and interface with the robot behaviour, hence improving the safety of the robot execution. The dynamic movement primitive (DMP) model is first employed to encode primitive skills, including those for both the translation and orientation. We have been utilized the behaviour tree (BT) to model the sequence of primitive skills. Since each node of the BT represents a single primitive skill, we can reproduce the BT nodes by employing different controllers based on the task requirements. We have evaluated the approach through two robot manipulation tasks, (i) grasping irregular objects with a customized soft suction cup and (ii) wiping whiteboard by a 7-DoF Frank Emika Panda. Results and performance analysis of the experiments are presented subsequently.