Precise and Dexterous Robotic Manipulation via Human-in-the-Loop
  Reinforcement Learning
Authors: name: Jianlan Luo, name: Charles Xu, name: Jeffrey Wu, name: Sergey Levine

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/.