English(EN)ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
新的世界-动作模型增强机器人操作和泛化能力
作者PulseAugur 编辑部·[8 个来源]·
研究人员开发了几种新的机器人操作世界-动作模型（WAMs），旨在提高效率和鲁棒性。LiLa-WAM专注于轻量级潜在推理空间，可在单个GPU上进行端到端训练，在基准任务上取得了很高的成功率。ST-WAM通过使用具有DINOv3特征和历史检索的语义-时间建模，增强了在视觉分布变化下的鲁棒性，在真实世界场景中表现明显优于先前模型。LAWM-3D从人类视频中学习3D感知潜在动作，通过多视图不变性和几何对齐来提高世界模型性能。XEWorld引入了一个测试平台，用于评估对未见过机器人实体的泛化能力，揭示了当前模型主要充当2D视觉模式匹配器。最后，MobileWAM通过融合视频扩散Transformer与动作专家并采用“先见之明链”（Chain-of-Foresight）方法进行运动和操作，将WAMs应用于移动操作。
AI
World models enable agents to perform forward rollout and planning without real-world interaction. However, their application in open-world embodied intelligence remains limited by the high cost of action annotations and the heterogeneity of action spaces across platforms. Recent…
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arXiv cs.CV
TIER_1English(EN)·Mingxin Wang, Bin Hu, Bin Qian, Kaitao Jiang, Haoning Wu, Feng Yan, Bowen Jing, Ruiyang Hao, Enyi Wang, Kangning Niu, Yandan Yang, Mu Xu, Yan Wang, Houde Liu, Tianlun Li·
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