EndoRSI：基于物理证据的内窥镜机器人策略递归改进
EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies
AI总结：
该研究提出人类管控的EndoRSI系统，利用物理实验证据迭代优化内窥镜机器人策略，使插入成功率从2/21提升至17/21，最终策略在多标本测试中表现最优，证明物理交互可用于智能体驱动的机器人策略研究。
摘要：
人工智能正开始实现科学发现的自动化，但物理机器人策略研究仍依赖科学家和工程师解读实验失败并决定如何修改控制器。我们提出EndoRSI（内窥镜递归自我改进系统，Endoscopic Recursive Self-Improvement），这是一个由人类管控的自我改进机器人研究系统，其中嵌入的EndoForge编码智能体利用物理实验的证据迭代开发和优化内窥镜机器人策略。物理执行始终隔离在安全约束的运行时环境中，且需获得人类授权。在机动内窥镜检查中，验证证据首先使智能体拒绝初始动作表示，随后的物理实验通过揭示源数据中不存在的危险闭环行为，证伪了离线支持的插入策略。失败的物理部署随后被纳入策略修订；插入成功率从第一代的2/21提升至第四代的17/21。冻结的最终策略在视觉和硬件变化下仍保持性能，且是六个独立离体猪标本中表现最佳的自主策略。这些结果表明，物理机器人交互可作为智能体驱动的机器人策略研究的实验证据。
英文摘要：
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.
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