title: Precise and Dexterous Robotic Manipulation via Human-in-the-Loop
    Reinforcement Learning
summary: 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/.
paper_authors._id: 672190cb52c4c6ffde46d129
paper_authors.name: Jianlan Luo
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-30T02:00:09.505Z
paper_authors._id: 64f8cb8ed04a890f5380d9a4
paper_authors.avatarUrl: /avatars/d6fdfdbb0c10141aa3b4c832d928121b.svg
paper_authors.fullname: Jianlan Luo
paper_authors.type: user
paper_authors.user: jianlanluo
paper_authors._id: 672190cb52c4c6ffde46d12a
paper_authors.name: Charles Xu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-30T02:18:37.616Z
paper_authors._id: 658fdaacac02633c0d78e3e8
paper_authors.avatarUrl: /avatars/c63481494f86368185a9e33587de77d3.svg
paper_authors.fullname: Charles Xu
paper_authors.type: user
paper_authors.user: charlesxu0124
paper_authors._id: 672190cb52c4c6ffde46d12b
paper_authors.name: Jeffrey Wu
paper_authors._id: 672190cb52c4c6ffde46d12c
paper_authors.name: Sergey Levine
paper_title: Precise and Dexterous Robotic Manipulation via Human-in-the-Loop
    Reinforcement Learning
paper_summary: 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/.
paper_ai_keywords: reinforcement learning (RL) human-in-the-loop vision-based RL dexterous manipulation tasks dynamic manipulation precision assembly dual-arm coordination demonstrations human corrections efficient RL algorithms robust policies adaptive policies reactive control strategies predictive control strategies imitation learning baselines