AI 中文总结
针对机器人演示数据中人类时序不适配的问题，提出接触感知的在线重定时层RoboPace，在保持几何路径下优化执行速度，兼顾接触安全与效率，无需重训练，显著提升成功率并缩短时间。
AI 中文摘要
机器人操作数据的采集正从遥操作转向无机器人演示，通过诸如通用操作接口（UMI）或直接来自人类手部等接口进行。基于此类数据训练的视觉-语言-动作（VLA）策略继承了演示者的时序。然而，人类时序并不能直接迁移到机器人：柔顺的手部能够容忍快速接触，而机器人可能因执行器和跟踪限制而出现超调；相反，机器人在自由空间中能够移动得更快。这促使我们提出一种统一的方法，以协调执行速度与接触安全性。我们提出了RoboPace，一种在线重定时层，它在保持策略几何路径的同时调整其时序，并尊重目标机器人的运动学和动力学约束。它基于预测的接触来调整执行速度，联合考虑接触相关的速度限制和机器人的运动约束。该方法无需重新训练策略，且能够实时运行。在双臂机器人的三个接触密集任务中，更快的均匀执行和仅考虑物理极限的重定时大多失败。相反，RoboPace在完成五个命令中的四个时，以大约一半的时间实现了比慢速均匀执行更高的整体成功率，同时保留了慢速执行的可靠性而不付出其时间代价。
英文摘要
Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manipulation Interface (UMI) or directly from human hands. Vision-Language-Action (VLA) policies trained on such data inherit the demonstrator's timing. Yet human timing does not directly transfer to robots: compliant hands tolerate fast contact, whereas robots may overshoot due to actuator and tracking limitations; conversely, robots can move faster in free space. This motivates a unified approach that reconciles execution speed with contact safety. We present RoboPace, an online retiming layer that preserves the policy's geometric path while adapting its timing, respecting the target robot's kinematic and dynamic constraints. It adapts execution speed based on predicted contact, jointly accounting for contact-dependent speed limits and the robot's motion constraints. The method requires no policy retraining and operates in real time. Across three contact-rich tasks on a dual-arm robot, faster uniform execution and physical-limit-only retiming largely fail. RoboPace instead achieves higher overall success than slow uniform execution while completing four of five commands in approximately half the time, retaining the reliability of slow execution without its time cost.
CommentsProject: https://robopace.airoa.io/
