Robot Learning

Intrinsic Motivation

Intrinsic motivation is the use of internally generated reward signals, such as curiosity, novelty, prediction error, or empowerment, to drive exploration in the absence of external task rewards. Methods like the Intrinsic Curiosity Module (ICM) and Random Network Distillation (RND) reward visiting hard-to-predict states, enabling agents to acquire diverse behaviors and data in sparse-reward or reward-free settings.

Why it matters for physical AI

Autonomous data collection at fleet scale cannot rely on hand-specified rewards for every skill, so intrinsic objectives offer a path to robots that practice and expand their own competence.