English(EN) Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies

新的EAPN方法增强了生成式机器人策略的一致性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 00:00

研究人员开发了一种名为执行对齐渐进噪声（EAPN）的新方法，以提高生成式机器人策略在连续异步重规划过程中的一致性。EAPN通过在块间和块内级别引入结构化随机性来解决模式切换等问题，确保新的动作块与实际执行对齐并保持时间相关性。在D3IL、Kinetix、LIBERO、物体存储和双臂折叠布料等各种模拟和现实世界任务上的评估表明，即使在推理延迟很长的情况下，行为一致性和任务成功率也得到了显著提高。 AI

影响增强了生成式机器人策略在现实世界应用中的可靠性和一致性。

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-05 00:00

面向一致异步再规划的生成式机器人策略的执行对齐渐进噪声

Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continuation across action chunks. We propose Execution-Aligned Progressive Noise (EAPN), which introduces …

本文如何被排名

报道来源 [1]

面向一致异步再规划的生成式机器人策略的执行对齐渐进噪声

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