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English(EN) Does Imitation Learning Preserve Temporal Robustness in Dexterous Manipulation? An Expert-Learner Comparison Across Task Execution Speeds

模仿学习在机器人操作中难以保持时间鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了模仿学习在机器人操作任务中保持时间鲁棒性的效果。该研究将一个专家机器人的表现与在一个名为 ParcelStow 的任务上，基于专家演示训练的 Action Chunking with Transformers (ACT) 策略进行了比较。虽然两者在标称速度下都达到了100%的成功率，但随着任务执行速度的增加，ACT 策略的成功率下降幅度远大于专家，表明其时间鲁棒性有所下降。 AI

影响 突出了模仿学习在需要动态适应的现实机器人应用中可能存在的局限性。

排序理由 该集群包含一篇详细介绍实验及其发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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模仿学习在机器人操作中难以保持时间鲁棒性

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该集群包含一篇详细介绍实验及其发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Clinton Enwerem, John S. Baras, Calin Belta ·

    模仿学习能否在灵巧操作中保持时间鲁棒性？一项跨任务执行速度的专家-学习者比较

    arXiv:2609.01453v1 Announce Type: cross Abstract: 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 o…

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