BRIDGE：面向物理AI的、通过形态-控制协同设计实现的开源人形机器人平台

中文摘要 AI 辅助

开发能够利用人类行为数据的人形机器人，对于实现通用具身智能至关重要，但传统开发受限于将硬件设计与全身控制相分离的解耦范式，该方法会导致系统性能欠佳，损害类人机器人的流畅性与敏捷性。为弥合这一差距，我们提出一种数据驱动的形态-控制协同设计框架，用于优化人形机器人的形态以实现类人运动。为量化形态保真度，我们还提出一种新指标，该指标同时考虑人类运动的运动学重定向保真度与动态跟踪性能。与基准人形机器人（Bumi、K1和Toddlerbot）相比，我们的框架在所有指标上均实现了最先进（SOTA）性能。最后，我们将该设计实现为Bridge，一款高88cm的开源人形机器人平台，随附其控制策略一同发布。我们证明Bridge能够以卓越保真度捕捉人类运动数据，在基础 locomotion、稳健平衡及高动态动作中展现出优异性能。视频及开源材料：this https URL

英文摘要

Developing humanoid robots capable of leveraging human behavioral data is essential for general-purpose embodiment, yet conventional development remains bottlenecked by a decoupled paradigm that isolates hardware design from whole-body control. This approach leads to suboptimal systems that compromise human-like fluidity and agility. To bridge this gap, we introduce a data-driven morphology-control co-design framework that optimizes humanoid morphology for human-like movement. To quantify morphological fidelity, we also introduce a novel metric that jointly considers kinematic retargeting fidelity to human motion and dynamic tracking performance. Our framework achieves state-of-the-art (SOTA) performance across all metrics compared to baseline humanoids (Bumi, K1, and Toddlerbot). Finally, we realize this design in Bridge, an open-source, 88cm-tall humanoid platform released alongside its control policy. We demonstrate that Bridge captures human motion data with superior fidelity, exhibiting exceptional performance across foundational locomotion, robust balance, and highly dynamic maneuvers. Videos and open-source materials: https://sites.google.com/view/bridgerobot.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）
• Huazhong University of Science and Technology（华中科技大学）
• JoyIn AI（追觅科技）

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