通过熵引导加速示范，让机器人操作快3倍还更准
DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
- 用生成策略估算每帧动作熵，区分关键与非关键帧
- 高熵帧加速下采样，低熵帧保持原速，整体提速3倍
- 适合想提升机器人执行速度且不牺牲成功率的开发者
模仿学习在机器人操作中展现巨大潜力，但因人类操作示范普遍过慢，导致策略执行效率低下。本文提出DemoSpeedup，一种自监督方法，通过熵引导加速视觉-运动策略。首先在正常速度示范上训练任意生成策略（如ACT或Diffusion Policy），用于估计每帧动作熵。低熵帧对应高精度操作，需保持稳定；高熵帧为较随意部分，可安全加速。据此对示范进行分段，并按熵值递增比例下采样加速。用加速后的示范训练策略，执行速度最高提升3倍，同时任务完成率不变甚至更高，得益于决策时域缩短带来的优势。
原文摘要 · Abstract (English)
Imitation learning has shown great promise in robotic manipulation, but the policy's execution is often unsatisfactorily slow due to commonly tardy demonstrations collected by human operators. In this work, we present DemoSpeedup, a self-supervised method to accelerate visuomotor policy execution via entropy-guided demonstration acceleration. DemoSpeedup starts from training an arbitrary generative policy (e.g., ACT or Diffusion Policy) on normal-speed demonstrations, which serves as a per-frame action entropy estimator. The key insight is that frames with lower action entropy estimates call for more consistent policy behaviors, which often indicate the demands for higher-precision operations. In contrast, frames with higher entropy estimates correspond to more casual sections, and therefore can be more safely accelerated. Thus, we segment the original demonstrations according to the estimated entropy, and accelerate them by down-sampling at rates that increase with the entropy values. Trained with the speedup demonstrations, the resulting policies execute up to 3 times faster while maintaining the task completion performance. Interestingly, these policies could even achieve higher success rates than those trained with normal-speed demonstrations, due to the benefits of reduced decision-making horizons. Project Page: https://demospeedup.github.io/
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