Robot Learning

State-Action Pair

A state-action pair is the tuple (s, a) coupling an environment state or observation with the action taken there, the atomic unit of experience in reinforcement and imitation learning. Demonstrations decompose into sequences of state-action pairs for behavior cloning, replay buffers store them within transition tuples (s, a, r, s'), and Q-functions assign values to them. Dataset scale in robot learning is often quoted in state-action pairs or transitions.

Why it matters for physical AI

Robot datasets, from teleoperated demos to fleet logs, are ultimately piles of state-action pairs; their quantity, diversity, and labeling quality bound what any policy can learn.