发表机构
Fudan University; Tsinghua University（复旦大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AdaTempo通过自监督学习示范中的共享相对节奏，加速视觉运动策略，实现高达3.57倍速度提升并改善成功-速度权衡。
AI 中文摘要
通过模仿学习训练的视觉运动策略常常继承了遥操作示范中不必要的缓慢时序。然而，统一的加速并不可靠，因为操作任务的不同阶段对加速的容忍度不同。在这项工作中，我们提出了AdaTempo，一种自监督方法，通过利用示范中的共享相对节奏结构来加速视觉运动策略。AdaTempo建立相位对应关系，将对齐的相对节奏聚合为共识，并将其映射为连续的加速曲线，用于将示范重采样为加速的训练轨迹。在这些重采样轨迹上训练标准策略（如ACT或Diffusion Policy）直接将所需节奏嵌入学习行为中，无需运行时节奏选择或在线重定时。大量评估表明，AdaTempo实现了高达3.57倍的加速，并产生了比原始策略和代表性加速基线更强的成功-速度权衡。
英文摘要
Visuomotor policies trained via imitation learning often inherit the unnecessarily slow timing of teleoperated demonstrations. Yet uniform speedup is unreliable because different phases of a manipulation task tolerate acceleration differently. In this work, we introduce AdaTempo, a self-supervised method that accelerates visuomotor policies by exploiting shared relative-tempo structure in demonstrations. AdaTempo establishes phase correspondence, aggregates the aligned relative tempo into a consensus, and maps it to a continuous speedup profile used to resample demonstrations into accelerated training trajectories. Training standard policies such as ACT or Diffusion Policy on these resampled trajectories directly embeds the desired tempo in the learned behavior, without runtime tempo selection or online retiming. Extensive evaluations show that AdaTempo achieves up to a $3.57\times$ speedup and yields a stronger success--speed trade-off than the original policies and representative acceleration baselines.
