Robot Learning

Causal Confusion

Causal confusion is a failure mode of imitation learning in which a policy latches onto spurious correlates of expert actions rather than their true causes, formalized by de Haan et al. (2019). A classic example is a driving policy attending to its own brake indicator light instead of the pedestrian that caused braking. Counterintuitively, giving the policy more observation history or richer inputs can worsen the problem by adding confounders.

Why it matters for physical AI

Behavior-cloned manipulation policies routinely exploit shortcuts like proprioceptive history or background cues, and diagnosing causal confusion guides choices about observation masking, history length, and data augmentation in large-scale training.