Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine
2024-10-30
Summary
This paper discusses a new approach called human-in-the-loop reinforcement learning (HITL RL) that helps robots learn complex manipulation skills more effectively by incorporating human feedback during training.
What's the problem?
Teaching robots to perform tasks like picking up objects or assembling items can be difficult, especially in real-world situations. Traditional reinforcement learning methods often struggle because they rely solely on automated processes, which can lead to slow learning and mistakes. Additionally, these methods may not adapt well to the complexities of real-world environments.
What's the solution?
The authors introduce a HITL RL system that combines human demonstrations and corrections with advanced reinforcement learning techniques. This system allows robots to learn from both their own experiences and direct input from humans, which helps improve their performance on various tasks, such as dynamic manipulation and precision assembly. The training process is efficient, taking only 1 to 2.5 hours to achieve high success rates. By integrating human guidance, the robots can learn faster and more accurately than with traditional methods.
Why it matters?
This research is important because it shows how combining human input with machine learning can significantly enhance robotic capabilities. By making robots better at complex tasks through HITL RL, we can improve their usefulness in industries like manufacturing, healthcare, and service, ultimately leading to more advanced and capable robotic systems.
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/.