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

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 through a three-stage pipeline spanning anatomical constraints, kinematic criteria, and dynamic criteria. To quantify morphological fidelity, we also introduce two novel metrics evaluating kinematic and dynamic similarity against authentic human data. 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, 80cm-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. Demos can be found in our website: https://sites.google.com/view/bridgerobot.

Keywords: Humanoid, Morphology-Control Co-design