CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators
State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpus of heterogeneous video data that contains rich signals for learning generalizable physics. To bridge this gap, we introduce CLAP, a framework for cross-embodiment action-conditioned video generation capable of being trained on diverse, internet-scale videos across human and robotic agents. CLAP is grounded in the insight that universal physical laws govern spatiotemporal dynamics regardless of the actor. However, cross-embodiment learning is non-trivial because action representations vary sharply across robot platforms and are typically ab...
| Authors | Kechen Liu, Ola Shorinwa |
|---|---|
| Primary category | cs.RO |
| Published | 27 August 2026 |
| Last updated at source | 11 September 2026 |
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