Precise and Dexterous Robotic Manipulation via Human-in-the-Loop
  Reinforcement Learning
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/.
672190cb52c4c6ffde46d129
Jianlan Luo
extracted_confirmed
2024-10-30T02:00:09.505Z
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/avatars/d6fdfdbb0c10141aa3b4c832d928121b.svg
Jianlan Luo
user
jianlanluo
672190cb52c4c6ffde46d12a
Charles Xu
extracted_confirmed
2024-10-30T02:18:37.616Z
658fdaacac02633c0d78e3e8
/avatars/c63481494f86368185a9e33587de77d3.svg
Charles Xu
user
charlesxu0124
672190cb52c4c6ffde46d12b
Jeffrey Wu
672190cb52c4c6ffde46d12c
Sergey Levine
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop
  Reinforcement Learning
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/.
A human-in-the-loop vision-based RL system achieves high performance on diverse robotic manipulation tasks with near-perfect success rates and fast training times.
reinforcement learning RL human-in-the-loop vision-based RL dexterous manipulation dynamic manipulation precision assembly dual-arm coordination demonstrations human corrections efficient RL algorithms robust policies adaptive policies reactive control predictive control