RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments

Key points

• A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse.1
• Robot agents that act through code can already repair programs from execution feedback, yet it remains a central challenge to organize this experience around the task structure that gives it meaning, so that each repair is attributed to the responsible capability, supported by execution evidence, and validated before it is reused.1
• Building upon this structure, a Manager, Planner, Engineer, and Reviewer coordinate planning, execution, diagnosis, and the validated release of new skills, while people steer the process through objectives and corrections; stable skill sequences are further consolidated into reusable compound skills.1
• On a mobile manipulator, RoboRSI develops multi-object household cleanup over 104 rounds.1

Sources (1)

• [1]RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:55 PM

  We introduce RoboRSI, a robot self-improvement system built on Top-Down Skill Refinement (TSR).

  A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse.

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