Robot Foundation Models
Overview
Robot foundation models aim to transfer capabilities across tasks, environments, and robot bodies by learning from large collections of robot demonstrations, human videos, and other multimodal data. Common approaches include vision-language-action policies, world-action models that predict how actions change scenes, and motion or tactile models, trained with imitation and self-supervision and often adapted through reinforcement learning or human feedback. Researchers evaluate them on manipulation and navigation benchmarks such as LIBERO and RoboTwin, as well as on real robots and humanoids, with growing attention to long-horizon behavior and cross-embodiment transfer. Broad generalization remains difficult: data must align with each robot’s actions and sensors, while robustness, safety, latency, and the work needed for deployment are not yet resolved by strong benchmark results alone.
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