Cross-Embodiment
Training a single robot policy that works across different hardware configurations (different arm DOFs, grippers, sensors, mobile bases). Like foundation models for text, cross-embodiment models (RT-2, CrossFormer, Open X-Embodiment) learn general manipulation skills from diverse robot data. Reduces the need to collect data per robot configuration separately.
In practice
A company with multiple robot models in production can train a single cross-embodiment model on all collected data, instead of maintaining separate policies for each robot. In practice, the Open X-Embodiment dataset aggregates over 1 million episodes from 22 different robots; a researcher can fine-tune this model on a few examples from their specific robot and achieve better performance than training from scratch.