Robot Learning

Observation History

Observation history is the window of past observations, and often past actions, provided to a policy at each decision step instead of only the current frame. History lets a policy infer velocities, contact states, and other partially observed quantities, implemented via frame stacking, recurrent networks, or transformer context. Many manipulation policies use short histories, since longer windows can encourage causal-confusion shortcuts.

Why it matters for physical AI

Real robots are partially observed, so temporal context is often required, but history length is a consequential design choice affecting both robustness and imitation-learning failure modes.