亚马逊FAR团队发布人形机器人OmniRetarget

DoNews2025-10-06

亚马逊机器人团队FAR（Frontier AI for Robotics）发布了其首个人形机器人研究成果OmniRetarget，实现无需感知单元的“盲眼”运动，在30秒内完成搬椅、爬桌、翻跟头等复杂动作。OmniRetarget是一个开源数据生成引擎，通过交互网格（interaction mesh）建模机器人、物体与地形间的空间和接触关系，将人类演示转化为适用于人形机器人的高质量运动学参考，支持...

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浪潮上的小舢板
2025-10-07
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16:28","market":"us","language":"zh","title":"亚马逊FAR团队发布人形机器人OmniRetarget","url":"https://stock-news.laohu8.com/highlight/detail?id=2573620175","media":"DoNews","summary":"亚马逊机器人团队FAR发布了其首个人形机器人研究成果OmniRetarget，实现无需感知单元的“盲眼”运动，在30秒内完成搬椅、爬桌、翻跟头等复杂动作。实验显示，搭载OmniRetarget的宇树G1成功执行持续30秒的多阶段跑酷任务。与PHC、GMR、VideoMimic等基线相比，OmniRetarget在穿透、脚部打滑和接触保留指标上整体更优，且轻微穿透可由强化学习修复。下游策略评估表明，OmniRetarget在所有任务中成功率领先基线10%以上，表现更稳定。该成果为人形机器人领域首次公开尝试。","content":"<html><body><article><p>亚马逊机器人团队FAR（Frontier AI for Robotics）发布了其首个人形机器人研究成果OmniRetarget，实现无需感知单元的“盲眼”运动，在30秒内完成搬椅、爬桌、翻跟头等复杂动作。</p><p>OmniRetarget是一个开源数据生成引擎，通过交互网格（interaction mesh）建模机器人、物体与地形间的空间和接触关系，将人类演示转化为适用于人形机器人的高质量运动学参考，支持全身控制中的“移-操一体”（loco-manipulation）技能，并实现从仿真到硬件的零样本迁移。</p><p>交互网格被定义为体积结构，顶点包括关键关节及物体与环境采样点，通过德劳内四面体化构建。算法最小化源动作与目标动作间的<a href=\"https://laohu8.com/S/688726\">拉普拉斯</a>形变能，以保持相对空间结构和接触关系。每帧通过求解约束非凸优化问题获得机器人配置，满足碰撞避免、关节限制和防滑等硬约束，采用顺序二次规划风格方法保证时间连续性和平滑性。</p><p>系统通过参数化改变物体配置、形状或地形特征，将单个人类演示扩展为多样化轨迹。在物体交互中，增强位姿与平移并固定下半身至标称轨迹，使上半身探索新协调方式；在地形交互中，调整平台高度与深度并引入额外约束生成多场景。强化学习用于弥补动力学差异，训练低层策略将运动学轨迹转化为物理可执行动作。</p><p>机器人仅依赖本体感知和参考轨迹作为先验知识，输入包括参考关节位置/速度、骨盆误差、骨盆线/角速度、关节状态及先前动作。奖励函数包含身体跟踪、物体跟踪、动作速率、软关节限制和自碰撞五类，并结合领域随机化提升泛化能力。相似动作分组训练以加速收敛，不同任务采用独立策略设置。</p><p>实验显示，搭载OmniRetarget的宇树G1成功执行持续30秒的多阶段跑酷任务。完整增强数据集上的训练成功率为79.1%，接近仅用标称动作的82.2%，表明增强显著扩展动作覆盖范围而不明显降低性能。与PHC、GMR、VideoMimic等基线相比，OmniRetarget在穿透、脚部打滑和接触保留指标上整体更优，且轻微穿透可由强化学习修复。</p><p>下游策略评估表明，OmniRetarget在所有任务中成功率领先基线10%以上，表现更稳定。实验基于OMOMO、内部MoCap和LAFAN1数据集进行。<a href=\"https://laohu8.com/S/AMZN\">Amazon</a> FAR成立七个多月，由前Covariant团队组建，核心成员包括Pieter Abbeel、Peter Chen、Rocky Duan和Tianhao Zhang，其中Rocky Duan任研究负责人。该成果为人形机器人领域首次公开尝试。</p></article></body></html>","source":"tencent","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" 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display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n亚马逊FAR团队发布人形机器人OmniRetarget\n</h2>\n\n<h4 class=\"meta\">\n\n\n2025-10-06 16:28 北京时间&nbsp;&nbsp;&nbsp;<a href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN2025100616320994bc96d9&s=b><strong>DoNews</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>亚马逊机器人团队FAR（Frontier AI for Robotics）发布了其首个人形机器人研究成果OmniRetarget，实现无需感知单元的“盲眼”运动，在30秒内完成搬椅、爬桌、翻跟头等复杂动作。OmniRetarget是一个开源数据生成引擎，通过交互网格（interaction mesh）建模机器人、物体与地形间的空间和接触关系，将人类演示转化为适用于人形机器人的高质量运动学参考，支持...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN2025100616320994bc96d9&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"LU2452424414.USD":"BGF ESG MULTI-ASSET \"A10\" (USDHDG) INC","LU0154236417.USD":"BGF US FLEXIBLE EQUITY \"A2\" ACC","BK4579":"人工智能","LU0965509101.SGD":"AB LOW VOLATILITY EQUITY PORTFOLIO \"A\" (SGDHDG) ACC","LU2211815571.USD":"ALLIANZ POSITIVE CHANGE \"AT\" (USD) ACC","IE00B7KXQ091.USD":"Janus Henderson Balanced A Inc USD","LU0130102774.USD":"Natixis Harris Associates US Equity RA 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mesh）建模机器人、物体与地形间的空间和接触关系，将人类演示转化为适用于人形机器人的高质量运动学参考，支持全身控制中的“移-操一体”（loco-manipulation）技能，并实现从仿真到硬件的零样本迁移。交互网格被定义为体积结构，顶点包括关键关节及物体与环境采样点，通过德劳内四面体化构建。算法最小化源动作与目标动作间的拉普拉斯形变能，以保持相对空间结构和接触关系。每帧通过求解约束非凸优化问题获得机器人配置，满足碰撞避免、关节限制和防滑等硬约束，采用顺序二次规划风格方法保证时间连续性和平滑性。系统通过参数化改变物体配置、形状或地形特征，将单个人类演示扩展为多样化轨迹。在物体交互中，增强位姿与平移并固定下半身至标称轨迹，使上半身探索新协调方式；在地形交互中，调整平台高度与深度并引入额外约束生成多场景。强化学习用于弥补动力学差异，训练低层策略将运动学轨迹转化为物理可执行动作。机器人仅依赖本体感知和参考轨迹作为先验知识，输入包括参考关节位置/速度、骨盆误差、骨盆线/角速度、关节状态及先前动作。奖励函数包含身体跟踪、物体跟踪、动作速率、软关节限制和自碰撞五类，并结合领域随机化提升泛化能力。相似动作分组训练以加速收敛，不同任务采用独立策略设置。实验显示，搭载OmniRetarget的宇树G1成功执行持续30秒的多阶段跑酷任务。完整增强数据集上的训练成功率为79.1%，接近仅用标称动作的82.2%，表明增强显著扩展动作覆盖范围而不明显降低性能。与PHC、GMR、VideoMimic等基线相比，OmniRetarget在穿透、脚部打滑和接触保留指标上整体更优，且轻微穿透可由强化学习修复。下游策略评估表明，OmniRetarget在所有任务中成功率领先基线10%以上，表现更稳定。实验基于OMOMO、内部MoCap和LAFAN1数据集进行。Amazon FAR成立七个多月，由前Covariant团队组建，核心成员包括Pieter Abbeel、Peter Chen、Rocky Duan和Tianhao Zhang，其中Rocky Duan任研究负责人。该成果为人形机器人领域首次公开尝试。","news_type":1,"symbols_score_info":{"USAW.SI":0.6,"AMZN":0.9}},"isVote":1,"likeStatus":false,"favoriteStatus":false,"reportStatus":false,"tweetType":1,"langContent":"CN"}],"isCommentEnd":false,"newsSizeData":{"likeSize":3,"commentSize":1,"repostSize":1,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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