用视觉反馈让手术机器人自动修正自身定位误差
Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning
- 通过内外视觉融合，实时补偿机器人内部传感器误差
- 在达芬奇研究套件上验证，控制精度显著提升
- 无需完美物理模型，适合实际手术场景部署
机器人辅助手术已成为现代微创手术的核心，自动化正成为提升精度、减轻术者疲劳的关键。然而，手术机器人固有的运动学不准确性严重制约了发展，其内部传感器不可靠导致控制误差。以往基于模型的校准方法成本高且效果有限。本文提出一种基于教师-学生学习框架的视觉闭环学习策略，使机器人能融合不可靠的内部读数与精确的外部视觉数据，在无需完美物理模型的前提下实时纠正运动学误差。该策略在达芬奇研究套件（da Vinci Research Kit）上成功部署，实验验证了利用外部视觉克服内部传感器缺陷的基本可行性。本研究为更先进、更可靠的手术自动化提供了基础控制方法。
原文摘要 · Abstract (English)
Robot-Assisted Surgery is integral to modern minimally invasive procedures, with automation emerging as the next frontier to enhance precision and reduce surgeon fatigue. This evolution is largely impeded by the inherent kinematic inaccuracies of surgical robots, where unreliable internal sensors lead to significant control errors. While previous methods attempted to mitigate these issues through complex model-based calibration, they often suffer from high cost and limited effectiveness. This work utilises a learning-policy to actively compensate for hardware inaccuracies using closed-loop visual feedback that was trained from a teacher-student learning framework. The policy can fuse unreliable internal readings with precise external visual data, allowing it to correct for kinematic errors in real time without needing a perfect physical model. The learned policy was successfully deployed on the da Vinci Research Kit, where experiments validated the fundamental feasibility of using external vision to overcome internal sensor deficits. This research provides a foundational and reliable control methodology, paving the way for more advanced and robust surgical automation.
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