让操作员实时接管机器人，提升视觉运动扩散策略的训练效果
Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
- 操作员可实时介入纠正机器人动作，再无缝交还控制权
- 使用接管示范后策略性能显著优于仅用初始示范训练
- 用马氏距离自动识别异常状态，适合机器人控制研究者
我们提出一种实时操作员接管（RTOT）范式，使操作员能够无缝接管正在运行的视觉运动扩散策略，引导系统回到理想状态或提供针对性修正示范。在该框架中，操作员可干预纠正机器人运动，随后控制权平滑返回策略，直至下一次需要干预。我们在刚体、柔性和颗粒物体三类任务上评估了该接管框架，结果表明，引入针对性接管示范能显著提升策略性能，优于仅使用等量初始示范训练的效果。此外，我们深入分析了马氏距离作为执行过程中识别异常或分布外状态的信号的有效性。相关支持材料，包括初始与接管示范视频及所有实验数据，均可在项目网站 https://operator-takeover.github.io/ 查看。
原文摘要 · Abstract (English)
We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirable states or providing targeted corrective demonstrations. Within this framework, the operator can intervene to correct the robot's motion, after which control is smoothly returned to the policy until further intervention is needed. We evaluate the takeover framework on three tasks spanning rigid, deformable, and granular objects, and show that incorporating targeted takeover demonstrations significantly improves policy performance compared with training on an equivalent number of initial demonstrations alone. Additionally, we provide an in-depth analysis of the Mahalanobis distance as a signal for automatically identifying undesirable or out-of-distribution states during execution. Supporting materials, including videos of the initial and takeover demonstrations and all experiments, are available on the project website: https://operator-takeover.github.io/
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