Endoscopic robot policies improve through physical experiment feedback
EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies
Robotics
Summary
Controlling robots inside the body, like those used for endoscopy, is tricky and needs constant adjustments. The authors designed a system called EndoRSI where a robot’s control programs get better by learning from real physical tests. The system safely runs experiments to see what works and what doesn’t, then changes its approach accordingly. Over four rounds of testing, the robot got much better at inserting itself without causing harm. This helps robots learn from the real world, not just simulations.
What this means in practice
- •For medical robotics engineers: Develop safer and more effective autonomous endoscopy robots by using physical test results to iteratively refine control policies.
- •For robot software developers: Create adaptive robot control programs that automatically improve through real-world experiment feedback under safety supervision.
Authors
Chi Kit Ng, Yidong Zhang, Tianchun Wu, Siu Hing Lui, Hanlin Zhao, Huxin Gao, Hongliang Ren
Abstract
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. 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. Physical execution remains isolated within a safety-constrained runtime under human authorisation. 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. Failed physical rollouts were then incorporated into policy revision; insertion success subsequently increased from 2/21 in the first generation to 17/21 in the fourth. The frozen final policy retained competence across visual and hardware shifts and was the best performing autonomous policy across six independent ex-vivo porcine specimens. These results show that physical robot interaction can serve as experimental evidence for agent-driven robot policy research.