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English(EN) AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation

人形机器人通过强化学习实现自主抓取与递送

研究人员开发了AdaptManip,一个用于人形机器人自主导航、抓取和递送物体的新型框架。与依赖人类演示的先前方法不同,AdaptManip在没有人类输入的情况下利用强化学习来训练一个鲁棒的策略。该系统集成了实时物体状态估计器、具有残差操作控制的全身运动策略以及基于LiDAR的定位系统,所有这些都在模拟环境中训练,并在真实硬件上进行零样本部署。 AI

影响 这项研究推进了人形机器人的自主能力,可能导致在物流和制造领域出现更通用的机器人助手。

排序理由 该集群描述了一篇详细介绍机器人操作新框架的研究论文。[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 →

人形机器人通过强化学习实现自主抓取与递送

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该集群描述了一篇详细介绍机器人操作新框架的研究论文。[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) · Morgan Byrd, Donghoon Baek, Kartik Garg, Hyunyoung Jung, Daesol Cho, Maks Sorokin, Robert Wright, Sehoon Ha ·

    AdaptManip:通过在线循环状态估计学习自适应全身物体抓取与运送

    arXiv:2602.14363v2 Announce Type: replace-cross Abstract: This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning-based …