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/

翻译：我们提出了一种实时操作员接管范式，使操作员能够无缝控制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有针对性的纠正示范。在此框架下，操作员可介入纠正机器人运动，随后控制权平稳交还给策略，直至需要进一步干预。我们在涉及刚体、变形体和颗粒物体的三项任务上评估了该接管框架，结果表明，与仅使用等量初始示范进行训练相比，融入有针对性的接管示范能显著提升策略性能。此外，我们深入分析了马哈拉诺比斯距离作为自动识别执行过程中不良或分布外状态的信号。支持材料（包括初始示范和接管示范的视频以及所有实验）可在项目网站上获取：https://operator-takeover.github.io/