BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI

2026-09-03

AI Summary

为解决人形机器人设计与控制分离导致的性能不佳问题，提出一种形态-控制协同设计框架，并开发了开源人形平台Bridge，实现更优的人类运动模仿。

Abstract

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.

Problem

• humanoid robots
• human behavioral data
• general-purpose embodiment
• morphology-control co-design
• human-like movement

Innovation

• data-driven morphology-control co-design
• kinematic retargeting fidelity
• dynamic tracking performance
• open-source humanoid platform