Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot
Yunlei Shi, Zhaopeng Chen, Hongxu Liu, Sebastian Riedel, Chunhui Gao, Qian Feng, Jun Deng, Jianwei Zhang
Abstract
Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we first consider incorporating operational space visual and haptic information into a reinforcement learning (RL) method to solve the target uncertainty problems in unstructured environments. Moreover, we propose a novel idea of introducing a proactive action to solve a partially observable Markov decision process (POMDP) problem. With these two ideas, our method can either adapt to reasonable variations in unstructured environments or improve the sample efficiency of policy learning. We evaluated our method on a task that involved inserting a random-access memory (RAM) using a torque-controlled robot and tested the success rates of different baselines used in the traditional methods. We proved that our method is robust and can tolerate environmental variations.