Robot Learning

Sample Efficiency

Sample efficiency is the amount of data or environment interaction a learning algorithm needs to reach a given level of performance. In robotics it is a first-order concern because real-world data collection is slow, hardware wears out, and failures can be destructive. Off-policy reinforcement learning, demonstration bootstrapping, pretrained representations, and simulation are the standard levers for improving it.

Why it matters for physical AI

Real robot time is the scarcest resource in physical AI; methods that learn from hundreds rather than millions of trials determine what is practical to train outside of simulation.