Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a reinforcement learning-based method for robotic peg-in-hole assembly that handles position uncertainty and accelerates training through transfer learning and domain randomization, demonstrating effectiveness in contact-rich tasks.

Contribution

The work presents a novel RL framework for position-controlled robots in peg-in-hole tasks, incorporating transfer learning and domain randomization to improve training efficiency and robustness.

Findings

Effective in contact-rich environments

Reduced training time via transfer learning

Robust performance with position uncertainty

Abstract

Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an unstructured environment remains an open problem. Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with position uncertainty of the hole. We proposed the use of an off-policy model-free reinforcement learning method and bootstrap the training speed by using several transfer learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated on contact-rich insertion tasks on a variety of environments.