Joining Force of Human Muscular Task Planning With Robot Robust and Delicate Manipulation for Programming by Demonstration
Fei Wang, Xingqun Zhou, Jianhui Wang, Xing Zhang, Zhenquan He, Bo Song
Abstract
Recently, programing by demonstration (PbD) received much attention for its capacity of fast programming with increasing demands in the robot manipulation area, especially in industrial applications. However, one of the biggest challenges of PbD is the recognition of demonstrator's finger high-fidelity motions especially in the environments with uncertainties, which limits the efficiency and accuracy of PbD. In this article, inspired by human dexterity, a novel PbD approach using the implicit muscular task planning strategy is presented to extract features from the arms' giant movement and the hands' fine motions during the demonstrator's operation. Furthermore, we integrate a deep reinforcement learning control method that further improves the manipulations' adaptive ability in the unknown or dynamic environments. The experimental results show that our proposed approach can deal with relative complex assembly tasks with a success rate of more than 67% within a fit tolerance of 4.2 mm by one-shot demonstration.