Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information

TL;DR

This paper presents a unified offline-to-online reinforcement learning framework with goal-aware state information and advanced representation learning, significantly improving data efficiency and performance in robotic manipulation tasks.

Contribution

It introduces a novel unified framework that mitigates transition performance drops and incorporates goal-aware states to enhance learning efficiency in robotic manipulation.

Findings

Outperforms state-of-the-art methods in multiple tasks

Reduces training time and data requirements

Improves policy robustness and success rates

Abstract

End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics. The latest methods that utilize human demonstration data and unsupervised representation learning has proven to be a promising direction to improve RL learning efficiency. The use of demonstration data also allows "warming-up" the RL policies using offline data with imitation learning or the recently emerged offline reinforcement learning algorithms. However, existing works often treat offline policy learning and online exploration as two separate processes, which are often accompanied by severe performance drop during the offline-to-online transition. Furthermore, many robotic manipulation tasks involve complex sub-task structures, which are very challenging to be solved in RL with sparse reward. In this work, we propose a unified offline-to-online RL framework that resolves the transition performance drop issue. Additionally, we introduce goal-aware state information to the RL agent, which can greatly reduce task complexity and accelerate policy learning. Combined with an advanced unsupervised representation learning module, our framework achieves great training efficiency and performance compared with the state-of-the-art methods in multiple robotic manipulation tasks.