Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning

Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine

Science Robotics 2025 article cited by 69

importance 6/10 partially solved Robot Manipulation and Learning industrial robotics / manufacturing stage: research

Robots struggle with precise, dexterous real-world manipulation (assembly, dynamic handling, dual-arm coordination) needed for reliable industrial deployment.

Reliable robotic manipulation underpins industrial automation productivity, yet existing methods need heavy manual design and large-scale data.

A human-in-the-loop, vision-based reinforcement learning system learned dexterous manipulation skills through interaction, claiming strong performance across precise assembly, dynamic manipulation, and dual-arm tasks.

Strong performance claimed on wide range of dexterous tasks reflecting industrial tolerances; no concrete metrics stated (abstract truncated)

Demonstrated an RL route to dexterous manipulation per abstract, but no quantitative benchmarks or deployment evidence stated.

Venue Science Robotics
Published 2025
Work type article
Authors Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine
Citations 69 (OpenAlex, at import time)
References 89
Model or method human-in-the-loop, vision-based reinforcement learning (no named model)
Organization not stated in source
Primary source https://doi.org/10.1126/scirobotics.ads5033
Open access green
Keywords reinforcement learning, robotic manipulation, human-in-the-loop, dexterous assembly, dual-arm coordination
6 / 10

significant — thousands to millions affected

Reliable dexterous manipulation could enable industrial automation at scale; abstract cites deployment barriers and industrial tolerances but states no scale of affected industry or workers.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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