REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation

TL;DR

This paper presents REBOOT, a system that leverages data reuse and bootstrapping in reinforcement learning to efficiently train dexterous robotic manipulation skills in the real world, reducing sample complexity and manual resets.

Contribution

It introduces a novel integration of sample-efficient RL and replay buffer bootstrapping with learned resets and rewards for real-world dexterous manipulation.

Findings

Enables fast learning of complex manipulation tasks with a four-fingered robotic hand.

Reduces the need for manual resets and reward engineering in real-world RL.

Demonstrates significant improvement in training efficiency through data reuse.

Abstract

Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance non-prehensile forces, and control large degrees of freedom. Reinforcement learning (RL) offers a promising approach due to its general applicability and capacity to autonomously acquire optimal manipulation strategies. However, its real-world application is often hindered by the necessity to generate a large number of samples, reset the environment, and obtain reward signals. In this work, we introduce an efficient system for learning dexterous manipulation skills with RL to alleviate these challenges. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. This combination allows us to utilize data from different tasks or objects as a starting point for training new tasks, significantly improving learning efficiency. Additionally, our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy as well as learned reward functions, eliminating the need for manual resets and reward engineering. We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand. (Videos: https://sites.google.com/view/reboot-dexterous)