Robot Learning

Action Horizon

An action horizon is the number of future timesteps covered by a policy's predicted action sequence, or executed before replanning. Chunked policies often distinguish a prediction horizon (how many actions the model outputs, commonly around 16 to 100 control steps) from an execution horizon (how many are actually applied before the next inference call). Longer horizons yield smoother, more committed behavior; shorter ones react faster to disturbances.

Why it matters for physical AI

Tuning the execution horizon is a core deployment knob: too long and the robot ignores perturbations mid-chunk, too short and inference latency dominates, so practitioners tune it per task and per platform.