模仿學習能否保留靈巧操作中的時間穩健性？跨任務執行速度的專家－學習者比較

摘要

透過模仿學習的靈巧操作策略，其穩健性通常以場景、物體或指令的變化來評估，但這些策略在不同任務執行速度下的表現則較少受到檢視。這使得學習者相對於其所模仿的專家能保留多少時間上的穩健性，仍是懸而未決的問題。我們在相同的任務條件、初始條件抽樣與加速倍數下，比較專家與學習者。我們將此評估具體實現在 ParcelStow 任務中，這是一項接觸密集的任務，機器人需抓取、重新定向並插入包裹。示範涵蓋了包裹抓取後各操作階段的加速倍數範圍。由專家示範訓練而來的腳本化專家，以及利用Transformer進行動作分塊（Action Chunking with Transformers, ACT）的策略，在標稱速度下皆達到 100% 的任務成功率。兩者的成功率在示範的速度範圍內出現分歧：在最大速度下，專家成功率為 84%，ACT 為 53%。兩個採用不同參數初始化的 ACT 策略展現出類似的性能退化，從標稱速度到最大示範速度分別下降了 34 與 48 個百分點，而專家僅下降 16 個百分點。階段層級分析顯示，ACT 在最大示範速度下的 47 次失敗中，有 35 次是插入錯位。在相對運動交接機制下，每一次 ACT 的抓取都能在自由空間中完成重新定向與轉移並保持對包裹的掌控，但只有 64% 能完成整體任務，相較之下，專家抓取後的完成率為 95%。在所有被評估的策略與速度中，沒有力閉合的 414 次抓取皆未能完成任務。因此，標稱任務成功率相同，並不代表在不同執行速度下仍能保持專家的表現。程式碼、資料與評估腳本可於 https://github.com/coenwerem/parcelstow 取得。

English

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.