Cross-embodiment

Cross-embodiment refers to training or transferring robot policies across multiple physical platforms. Different [degrees of freedom](/glossary/degrees-of-freedom), different actuator types, different sensor suites. The strict version: one policy that runs unmodified on platforms A, B, and C. Looser versions: policies trained on multi-platform datasets and then fine-tuned per platform; policies that share representations but use platform-specific action decoders.

The distinction matters because cross-embodiment is the central scaling claim of [foundation-model-for-robotics](/glossary/foundation-model-for-robotics) work. Physical Intelligence's π0 and π0.5 papers, Google DeepMind's RT-X and Open X-Embodiment dataset, and Skild AI's brain models all claim cross-embodiment generalization. The strict-vs-loose split mirrors the [zero-shot generalization](/glossary/zero-shot-generalization) split: a model that requires per-platform fine-tuning is making a meaningfully weaker claim than one that doesn't. Cross-embodiment without methodology details defaults to the loose version.