外观
论文arXiv 2610.0573423 天前
FreeSpeed：面向生成式机器人策略的免训练速度控制
原文链接：FreeSpeed: Training-Free Speed Control for Generative Robot Policies（arXiv 2610.05734 · 发布日期：2026-10-05）
FreeSpeed: Training-Free Speed Control for Generative Robot Policies
FreeSpeed通过后处理预训练策略的动作块，免训练实现在线执行速度控制，并在仿真与真实操作任务中验证。
核心要点
- 模仿学习策略继承示范执行速度，测试时改速可能引入分布外观测并降低成功率。
- 方法利用相邻动作方向不一致性判断任务阶段关键性，自适应重采样和缩放动作块。
- 在三类策略和50个仿真任务中，FreeSpeed支持在线变速，保成功设置下速率为0.22x至2.53x。
- 在四个真实操作任务中，平均成功率94.0%，接近冻结策略93.8%，速率为0.38x至1.97x。
验证范围与局限：验证范围限于三类策略的50个仿真任务和四个真实操作任务。
英文摘要 (Abstract)
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.
作者：Yuxuan Hu, Shilin Shan, Qiheng Wang, Jinghan Yang, Junqiao Fan, Hao Wan, Jianfei Yang
来源
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