English(EN) De-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control

新的机器人控制方法DirectWAM将推理时间缩短了28倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了DirectWAM，一种新的机器人控制方法，可在保持高任务成功率的同时显著降低推理延迟。该方法将预训练的视频和动作专家转换为单步预测器，绕过了扩散模型昂贵的迭代推理。DirectWAM在LIBERO和RoboTwin 2.0等基准套件上取得了卓越的性能，并在实际机器人任务中证明了其有效性，与迭代方法相比，推理时间缩短了28倍。 AI

影响 DirectWAM的效率提升可能会加速先进AI在实际机器人应用中的普及。

排序理由发表了一篇详细介绍机器人控制新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Shaoyi Zheng, Zhongtao Jiang, Yue Peng, Wenbo Lu, Shunchi Zhang, Xi Wang, Shengji Wan · 2026-10-21 04:00

去扩散WAM：机器人控制的动作充分直接预测

arXiv:2610.28122v1 Announce Type: cross Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We ask what a single-step predicted future must preserve to supp…

本文如何被排名

报道来源 [1]

去扩散WAM：机器人控制的动作充分直接预测

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