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English(EN) Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers

新型Transformer框架提升机器人灵巧性和协作能力

研究人员开发了一个新的框架,利用空间条件多智能体Transformer(MAT)来解决分布式灵巧操作(DDM)的复杂性。该方法专为涉及多个机器人(特别是64个软Delta机器人网格)的系统设计,以学习鲁棒的控制策略。该框架包含一个具有自适应层归一化的MAT以提高效率,空间对比嵌入以将Transformer嵌入与机器人位置关联,以及通过Soft Actor Critic微调的行为克隆方法。实验表明,MAT能够迭代地优化动作,并且空间条件有助于学习DDM策略,从而能够完成长时程操作任务,同时减少机器人使用量并最小化误差。 AI

影响 引入了一种新颖的基于Transformer的多机器人协调方法,有望推进自主操作能力。

排序理由 学术论文,详细介绍了一种新颖的机器人操作框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型Transformer框架提升机器人灵巧性和协作能力

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学术论文,详细介绍了一种新颖的机器人操作框架。[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) · Sarvesh Patil ·

    基于空间条件的多智能体Transformer的分布式灵巧操作

    arXiv:2609.06930v1 Announce Type: cross Abstract: Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-robot cooperation, and dynamic object-robot interactions. This paper introduces a…