发表机构
Nanyang Technological University; ROKAE Robotics（南洋理工大学; 珞石机器人）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FreeSpeed利用动作块方向不一致性作为信号，无需训练即可调整预训练策略的执行速度，在模拟和真实任务中保持成功率的同时实现0.22x至2.53x的速率变化。
AI 中文摘要
在线控制执行速度对于在真实世界场景中部署机器人策略至关重要，因为机器人可能需要在时间限制下加速，或减速以促进人类交互并提高安全性。然而，模仿学习策略继承了其演示的执行速度，测试时的速度修改可能引入不可恢复的分布外观测，从而降低任务成功率。我们观察到，动作块的方向不一致性反映了任务阶段的临界性，指示了在保持任务成功的同时，动作步长可以被修改的激进程度。基于这一观察，我们引入了FreeSpeed，一个无需训练的模块，用于后处理预训练策略的动作块。FreeSpeed以请求的速率重新采样每个预测块，然后使用相邻动作之间的方向不一致性作为重新缩放的主要信号。该信号自适应地确定执行速度可以接近请求速度的程度，允许在评估限制内灵活调整速度而不损害任务成功。在三个策略家族和50个模拟任务中，FreeSpeed支持在线速度变化，在保持各任务成功的设置中，实现的执行速率范围从0.22倍到2.53倍。在四个真实世界操作任务中，FreeSpeed实现了94.0%的平均成功率，与冻结策略的93.8%相当，同时实现了从0.38倍到1.97倍的执行速率。
英文摘要
Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constraints or slow down to facilitate human interaction and improve safety. However, imitation-learned policies inherit the execution speed of their demonstrations, and test-time speed modification can introduce unrecoverable out-of-distribution observations, reducing task success. We observe that the directional inconsistency of action chunks reflects task-phase criticality, indicating how aggressively action step lengths can be modified while preserving task success. Based on this observation, we introduce FreeSpeed, a training-free module that post-processes action chunks from pretrained policies. FreeSpeed resamples each predicted chunk at the requested rate, then uses directional inconsistency between adjacent actions as the primary signal for rescaling. This signal adaptively determines how closely the execution speed can approach the requested speed, allowing flexible speed adjustment within the evaluated limits without compromising task success. Across three policy families and 50 simulated tasks, FreeSpeed supports online speed changes, with realized execution rates spanning 0.22x to 2.53x among settings that preserve per-task success. Across four real-world manipulation tasks, FreeSpeed achieves an average success rate of 94.0%, matching the frozen policy's 93.8%, while realizing execution rates from 0.38x to 1.97x.
Comments37 pages, 21 figures, 14 tables. Project page: https://yuxuanhu9.github.io/FreeSpeed/
