Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning

摘要

This article proposes a platform for robots to learn disassembly tasks based on reinforcement learning (RL) techniques. The platform is demonstrated by a robot learning the skill of removing a bolt along a door-chain groove in a data-driven way, where the clearance between the bolt and the groove is less than 1 mm. Furthermore, the relationship between the performance of the learned skills and the precision of the robot is studied with a method to control the robot's precision by adding uncorrelated zero-mean Gaussian noise to the robot's actions. Finally, the transferability of the learned skills among robots with different precisions is empirically studied. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision.