Overview

This work investigates embodiment scaling laws in robotics, hypothesizing that training a single control policy on a larger number of diverse robot embodiments improves its ability to generalize to unseen ones.

Generating 1000 Robots

To study the effects of embodiment scaling, we procedurally generate GENBOT-1K dataset consisting if approximately 1,000 varied robot embodiments, including humanoids, quadrupeds, and hexapods, with different geometry, topology, and kinematics.

Embodiment Scaling Laws

Training generalist locomotion policies on subsets of GENBOT-1K shows that generalization to unseen robots improves steadily as the number of training embodiments increases.

Real-world Deployment

We confirm our results, by zero-shot transferring the learned unified policy to the Unitree Go2 quadruped and the Unitree H1 humanoid robots in the real world.

Acknowledgments

This research is funded by the NSF AI-Center TILOS, the Hillbot Embodied AI Fund, the National Science Centre Poland (Weave programme UMO-2021/43/I/ST6/02711), and by the German Science Foundation (DFG) (grant number PE 2315/17-1).
Co-author Hao Su is the CTO for Hillbot and receives income. The terms of this arrangement have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies.
We thank the German Research Center for AI (DFKI), Research Department: Systems AI for Robot Learning, for lending the Unitree Go2 and Unitree H1 robots.
Finally, we thank Oleg Kaidanov (DFKI, TU Darmstadt) for his continuous help with the real-world robot deployment.

Citation

@misc{ai2025embodimentscalinglaws,
    title={Towards Embodiment Scaling Laws in Robot Locomotion},
    author={Bo Ai and Liu Dai and Nico Bohlinger and Dichen Li and Tongzhou Mu and Zhanxin Wu and K. Fay and Henrik I. Christensen and Jan Peters and Hao Su},
    year={2025},
    eprint={2505.05753},
    archivePrefix={arXiv},
    primaryClass={cs.RO},
    url={https://arxiv.org/abs/2505.05753}, 
}