用统一动作空间让机器人模型跨平台通用，训练更高效。
Universal Actions for Enhanced Embodied Foundation Models
- 构建通用动作空间，通过共享结构特征提取跨机器人通用行为
- 0.5B模型在多机器人任务上超越14倍大的现有模型
- 适合需要快速适配新机器人的研究与工业应用
近期大型基础模型的成功得益于多样化的互联网规模数据训练。然而，将相同方法用于具身智能体时面临显著挑战。尽管存在大量众包具身数据集，由于不同机器人物理形态和控制接口差异，其动作空间存在显著异质性，阻碍了跨领域数据的有效利用与具身基础模型的发展。本文提出UniAct，一种运行于通用动作空间的具身基础建模框架。所学通用动作通过挖掘不同机器人的共享结构特征，捕捉跨机器人通用原子行为，消除异质性，提升跨领域数据利用率与跨具身泛化能力。通用动作可仅通过添加具身特异性信息，高效映射回具体动作指令，实现对新机器人的快速适应。我们0.5B参数规模的UniAct实例在多种真实与仿真机器人上评估中，表现优于14倍大的现有最先进模型，展现出卓越的跨具身控制与适应能力，凸显通用动作的关键优势。
原文摘要 · Abstract (English)
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticeable difficulties. Despite the availability of many crowd-sourced embodied datasets, their action spaces often exhibit significant heterogeneity due to distinct physical embodiment and control interfaces for different robots, causing substantial challenges in developing embodied foundation models using cross-domain data. In this paper, we introduce UniAct, a new embodied foundation modeling framework operating in a Universal Action Space. Our learned universal actions capture the generic atomic behaviors across diverse robots by exploiting their shared structural features, and enable enhanced cross-domain data utilization and cross-embodiment generalizations by eliminating the notorious heterogeneity. The universal actions can be efficiently translated back to heterogeneous actionable commands by simply adding embodiment-specific details, from which fast adaptation to new robots becomes simple and straightforward. Our 0.5B instantiation of UniAct outperforms 14X larger SOTA embodied foundation models in extensive evaluations on various real-world and simulation robots, showcasing exceptional cross-embodiment control and adaptation capability, highlighting the crucial benefit of adopting universal actions. Project page: https://github.com/2toinf/UniAct
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