Robot Learning

Policy Rollout

A policy rollout is the execution of a policy from an initial state for an episode or fixed horizon, recording the resulting trajectory of observations, actions, and rewards. Rollouts serve two roles: generating training experience for reinforcement learning, and evaluating a trained policy's success rate. Simulated rollouts are cheap and parallelizable, while real-robot rollouts are slow, risky, and require resets, motivating careful evaluation protocols.

Why it matters for physical AI

Real-world evaluation bandwidth is a genuine bottleneck for robot foundation models: statistically meaningful success rates demand many physical rollouts, so rollout-efficient evaluation and trustworthy sim proxies are active research problems.