Robot Learning

Structured Prediction

Structured prediction is machine learning that outputs structured objects — sequences, trees, graphs, or segmentations — whose components have mutual dependencies, rather than independent scalar labels. Classical formulations include conditional random fields and structured SVMs; in modern practice, autoregressive decoding and diffusion serve the same role. Robotics outputs are inherently structured: action sequences, grasp poses on SE(3), scene graphs, and trajectories all demand coherent joint prediction rather than independent guesses.

Why it matters for physical AI

Action chunking, trajectory generation, and scene graph inference are structured prediction problems in disguise; recognizing this explains why sequence models and diffusion dominate modern policy architectures.