mlsanityData to 7 Oct 2026

Towards Embodiment Scaling Laws in Robot Locomotion

arXiv 2505.05753 ↗First posted 9 May 2025cs.RO cs.AI cs.LGcited by 12

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Abstract (arXiv)

Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodiment scaling laws, the hypothesis that increasing the number of training embodiments improves generalization to unseen ones, using robot locomotion as a test bed. We procedurally generate ~1,000 embodiments with topological, geometric, and joint-level kinematic variations, and train policies on random subsets. We observe positive scaling trends supporting the hypothesis, and find that embodiment scaling enables substantially broader generalization than data scaling on fixed embodiments. Our best policy, trained on the full dataset, transfers zero-shot to novel embodiments in simulation and the real world, including the Unitree Go2 and H1. These results represent a step toward general embodied intelligence, with relevance to adaptive control for configurable robots, morphology co-design, and beyond.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 88 of its 119 references that are in the map. First posted on arXiv 9 May 2025.

Builds on

88 of 119 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 9.

12 papers.