Robot Learning

Zero-Shot Transfer

Zero-shot transfer is the deployment of a trained model in a new domain, embodiment, or environment without any adaptation data, most prominently sim-to-real transfer in which policies trained entirely in simulation run directly on hardware. Domain randomization over physics and appearance is the standard enabler, with quadruped locomotion policies among the clearest successes; cross-embodiment transfer between different robots is a newer frontier.

Why it matters for physical AI

Every hour of real-robot fine-tuning is expensive and risky, so the degree to which simulation-trained or cross-robot policies transfer untouched sets the economics of scaling physical AI.