用深度学习不确定性调控机器人自主性，提升安全可靠操作。
SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
- 根据感知置信度动态切换半自主与遥操作模式
- 在15人用户测试中实现高鲁棒性空中抓取任务
- 适合工业场景中对安全性要求高的机器人系统
深度学习虽推动了机器人感知进步，但其鲁棒性不足与可解释性差限制了在安全关键场景的应用。本文提出感知共享自治概念：利用基于神经正切核（NTK）的点云配准方法估算深度学习感知不确定性，当感知置信度高时启用半自主操作以提升性能；当不确定性上升时自动切换至触觉遥操作以保障可靠性。在包含15名参与者的人机测试及模拟工业场景中验证，即使深度学习感知失败，系统仍能稳定完成复杂空中操作任务。所构建系统SPIRIT显著提升了操作性能与系统可靠性，并入选一项重要工业创新奖决赛。
原文摘要 · Abstract (English)
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.
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