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Abstract:Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data sharing and control transfer. We ask: What structure can be shared across embodiments despite these differences? We argue that the physical interactions they induce can be modeled in a shared geometric space, allowing world models to provide a common interface for learning and control. To realize this idea, we represent human and robot hands as sets of 3D particles and define actions as end-effector particle displacement fields. This representation abstracts away embodiment-specific joint spaces while preserving the geometry and motion relevant to physical interaction. We train a graph-based world model on random interaction data from diverse simulated robot hands and real human hands, and integrate it with model-predictive control for deployment on new hardware. Experiments on rigid and deformable manipulation reveal three findings: increasing the diversity of training embodiments improves generalization to unseen hands; appropriately combining simulated and real-world data outperforms either source alone; and the same learned model enables effective control on robotic hands with distinct kinematics and degrees of freedom. These results position particle-based world models as a shared interface for learning from and for heterogeneous embodiments.

Submission history

From: Zihao He [view email]
[v1] Mon, 3 Nov 2025 03:02:16 UTC (2,066 KB)
[v2] Sun, 9 Nov 2025 06:52:01 UTC (2,032 KB)
[v3] Tue, 21 Jul 2026 02:42:52 UTC (3,487 KB)