Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

TL;DR

This paper introduces a human-in-the-loop reinforcement learning system that enables robots to learn complex dexterous manipulation tasks efficiently in real-world settings, achieving high success rates and fast training times.

Contribution

The work presents a novel integrated RL approach combining demonstrations, human corrections, and system design for real-world robotic manipulation, outperforming previous methods.

Findings

Achieved near-perfect success rates in diverse manipulation tasks

Reduced training time to 1-2.5 hours for complex skills

Outperformed imitation learning and prior RL approaches in success and speed

Abstract

Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices to learn policies that achieve near-perfect success rates and fast cycle times within just 1 to 2.5 hours of training. We show that our method significantly outperforms imitation learning baselines and prior RL approaches, with an average 2x improvement in success rate and 1.8x faster execution. Through extensive experiments and analysis, we provide insights into the effectiveness of our approach, demonstrating how it learns robust, adaptive policies for both reactive and predictive control strategies. Our results suggest that RL can indeed learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements. Videos and code are available at our project website https://hil-serl.github.io/.