外观
论文arXiv 2610.1816315 天前
RESTA：用于数据高效机器人模仿学习的时间缩放增强
原文链接：RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning（arXiv 2610.18163 · 发布日期：2026-10-13）
RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning
RESTA通过轨迹反转与时间重采样增强，提升机器人模仿学习数据效率，并在16个成对双臂操作任务上验证。
核心要点
- 模仿学习常依赖单方向、单时间分辨率的高成本示范。
- RESTA用轨迹反转与时间步长重采样复用示范并调节时序。
- 反转被视为成对任务近似先验，而非接触操作的物理对称。
- 低数据下反转尤其有效，随机时序下步长增强提高成功率。
验证范围与局限：验证范围为16个成对双臂操作任务。
英文摘要 (Abstract)
Robot manipulation policies trained through imitation learning commonly rely on costly demonstrations recorded in one task direction and at one temporal resolution. We propose Reversal and Stride-based Trajectory Augmentation (RESTA), which exploits time-scaling data augmentation to improve data efficiency through trajectory reversal and temporal resampling. Trajectory-reversal augmentation reverses and relabels demonstrations between paired tasks with opposite objectives, reusing recorded rollouts without additional environment interaction. Because contact-rich manipulation is not exactly reversible, we treat reversal as an approximate paired-task prior rather than a physical symmetry. Temporal-stride augmentation varies observation and action sampling strides and conditions the policy on the resulting offsets, enabling fixed or randomized temporal schedules. Experiments across 16 paired bimanual manipulation tasks show that trajectory reversal is particularly effective in the low-data regime and that temporal-stride augmentation improves success under randomized temporal schedules. With 25 and 50 independently collected demonstrations per task, RESTA achieves 78.4% and 85.2% average success, respectively, matching or exceeding baselines trained with twice as many demonstrations (77.3% and 85.4%). These results show that RESTA can improve data efficiency in robot imitation learning.
作者：Yunpeng Jiang, Jianshu Hu, Han Fang, ZhiYuan Guan, Paul Weng, Yutong Ban
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