Robot Learning

Language-Conditioned Policy

A language-conditioned policy is a robot control policy that takes a natural-language instruction as an input alongside observations, producing actions that accomplish the commanded task. Instructions are typically encoded with a pretrained language or vision-language model and fused with visual features, as in BC-Z, RT-1, and RT-2. A single network can thereby perform many tasks, with the instruction selecting the behavior at inference time.

Why it matters for physical AI

Language is the most practical task-specification interface for general-purpose robots, letting one foundation model cover thousands of tasks and enabling non-experts to retask hardware without programming or new demonstrations.