Cross-embodiment

Cross-embodiment refers to training or adapting robotic policies across different physical platforms. Instead of developing a control model for a single robot, engineers aim to generalize behaviors across bodies with varying limb lengths, joint types, or actuation systems. The goal is to reduce per-robot training cost and improve skill transfer.

Recent models like RT-X and Open X-Embodiment use multi-robot datasets to align control across morphologies. Policies are conditioned on embodiment-specific inputs while sharing structure across tasks. In simulation, this enables curriculum learning and zero-shot transfer. In real-world use, it supports faster deployment across fleets or hardware revisions.

Cross-embodiment is now central to scaling humanoid intelligence: when one robot learns, the whole system improves.