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This paper introduces a data-driven morphology-control co-design framework that integrates hardware design with whole-body control to optimize humanoid robot morphology for human-like movement. By developing a novel metric that assesses both kinematic retargeting fidelity and dynamic tracking performance, the authors achieve state-of-the-art results in humanoid robotics. The resulting platform, Bridge, is an open-source humanoid robot that demonstrates superior fidelity in capturing human motion and excels in locomotion, balance, and dynamic maneuvers compared to existing models.
Bridge outperforms existing humanoid robots by seamlessly integrating morphology and control, achieving unmatched fidelity in human motion replication.
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.
