Robot Learning

Goal Image Conditioning

Goal image conditioning is a policy conditioning scheme in which the desired task outcome is specified by an image of the goal state, such as a photograph of the assembled object or the target scene arrangement, and the policy learns to act so the observation comes to match it. Goal images sidestep language ambiguity and reward engineering, can be generated by video-prediction or subgoal models as in SuSIE, and enable hindsight relabeling of any trajectory.

Why it matters for physical AI

Goal images provide dense, self-supervised task specification that scales with unlabeled data, though capturing a goal photo at deployment is often awkward, so many systems combine image goals with language.