Robot Learning

Goal-Conditioned Policy

A goal-conditioned policy is a control policy that takes a goal specification, such as a target state, image, point, or language command, as an additional input, so a single network can perform many tasks by varying the goal. Training approaches include goal-conditioned behavior cloning, reinforcement learning with universal value functions, and hindsight relabeling, where achieved outcomes are retroactively treated as intended goals to densify supervision.

Why it matters for physical AI

Generalist robots must execute arbitrary commands rather than one hardwired task; goal conditioning is the basic mechanism that turns a policy into an instructable system and underlies every language-directed robot model.