本文探讨了通过模仿学习得到的灵巧操作策略在不同任务执行速度下的时间鲁棒性。研究对比了专家与学习者在相同任务条件、初始条件抽取和加速因子下的表现。实验在ParcelStow任务中展开，该任务涉及机器人获取、重新定位和插入包裹。结果显示，在标准速度下，专家和学习者均实现了100%的任务成功率，但在加速条件下，专家的成功率为84%，而学习者的成功率为53%。学习者在加速条件下的表现下降幅度较大，与专家相比，下降幅度分别为34和48个百分点。分析表明，学习者在加速条件下的失败主要发生在插入定位错误上。
要点：
1. 研究对比了专家和学习者在不同任务执行速度下的表现。
2. 在标准速度下，专家和学习者均实现了100%的任务成功率。
3. 在加速条件下，专家的成功率为84%，而学习者的成功率为53%。
4. 学习者在加速条件下的表现下降幅度较大，与专家相比，下降幅度分别为34和48个百分点。
5. 学习者在加速条件下的失败主要发生在插入定位错误上。
Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in scenes, objects, or instructions, but their performance across task execution speeds is less often examined. This leaves open how much temporal robustness a learner retains relative to the expert it imitates. We compare an expert and learner under the same task conditions, initial-condition draws, and speedup factors. We instantiate the evaluation in ParcelStow, a contact-rich task in which the robot acquires, reorients, and inserts a parcel. The demonstrations span the speedup range for the manipulation phases after parcel acquisition. A scripted expert and an Action Chunking with Transformers (ACT) policy trained from the expert's demonstrations both achieve 100 percent task success at nominal speed. Their success rates diverge within the demonstrated range: at its maximum, expert success is 84 percent and ACT success is 53 percent. Two ACT policies with different parameter initializations show similar degradation, decreasing by 34 and 48 percentage points from nominal speed to the maximum demonstrated speed, compared with 16 points for the expert. Stage-level analysis shows that 35 of ACT's 47 failures at the maximum demonstrated speed are insertion misalignments. Under the relative-motion handoff, every ACT acquisition retains the parcel through reorientation and transfer in free space, but only 64 percent complete the overall task, compared with 95 percent after expert acquisition. Across all evaluated policies and speeds, none of the 414 acquisitions without force closure completes the task. Equal nominal task success therefore does not imply preservation of expert performance across execution speeds. Code, data, and evaluation scripts are available at https://github.com/coenwerem/parcelstow.
原始发布方：HuggingFace Daily Papers（社区热门论文）
原文时间：2026-09-01 08:00:00 +08:00
