Robot Learning

Co-Training

Co-training in robot learning is the practice of training a single policy or model jointly on multiple data sources, such as data from different tasks, robots, simulators, or human video, so that shared structure improves each individual domain. Examples include RT-2's co-training on web vision-language data with robot trajectories and multi-robot co-training across embodiments in Open X-Embodiment. Careful data mixture weighting is typically decisive for final performance.

Why it matters for physical AI

Robot data is scarce, so co-training with web-scale vision-language corpora and cross-robot datasets is currently the dominant strategy for injecting broad semantics and generalization into robot foundation models.