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English(EN) ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies

ReGuide框架通过自改进引导来改进扩散策略

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ReGuide，一个旨在提高行为克隆扩散策略性能的新框架，这类策略通常容易受到协变量偏移的影响。ReGuide利用相位条件引导（PCG）通过在任务执行的特定阶段应用定向引导来生成纠正性推广。然后，通过微调（ReGuide-FT）或从头开始重新训练（ReGuide-FS）将这些成功的引导式推广整合回策略中，从而实现自我改进。在RoboMimic Can、Square、Transport和Tool Hang等基准测试上的实验表明，ReGuide显著提高了基础策略的成功率，在测试时纠正场景中优于现有方法。 AI

影响 增强了扩散策略对协变量偏移的鲁棒性，有望提高机器人任务在现实世界中的成功率。

排序理由 该集群包含一篇详细介绍改进扩散策略新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ReGuide框架通过自改进引导来改进扩散策略

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该集群包含一篇详细介绍改进扩散策略新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tzu-Hsiang Lin, Srinivas Shakkottai, Dileep Kalathil, P. R. Kumar ·

    ReGuide：从测试时引导到自改进扩散策略

    arXiv:2606.28939v1 Announce Type: new Abstract: Behavior-cloned diffusion policies are expressive but remain vulnerable to covariate shift: small deviations from demonstrated states can compound into task failure. Existing methods address this either by expanding the training dis…

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