EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies

Key points

• Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to interpret experimental failures and decide how controllers should be revised.1
• Physical execution remains isolated within a safety-constrained runtime under human authorisation.1
• In motorised endoscopy, validation evidence first caused the agent to reject an initial action representation, while subsequent physical experiments falsified an offline-supported insertion strategy by revealing hazardous closed-loop behaviour absent from the source data.1
• These results show that physical robot interaction can serve as experimental evidence for agent-driven robot policy research.1

Sources (1)

• [1]EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:49 AM

  We present EndoRSI (Endoscopic Recursive Self-Improvement), a human-governed, self-improving robotic research system in which the embedded EndoForge coding agent iteratively develops and refines endoscopic robot policies using evidence from physical experiments.

  Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to interpret experimental failures and decide how controllers should be revised.

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