Robot Learning

Curiosity-Driven Exploration

Curiosity-driven exploration is a reinforcement learning technique that supplements or replaces external rewards with intrinsic rewards for encountering novel or poorly predicted states, encouraging systematic exploration in sparse-reward settings. Notable formulations include prediction-error curiosity via the Intrinsic Curiosity Module (Pathak et al., 2017) and Random Network Distillation. Care is required to avoid pathologies like the noisy-TV problem, where irreducible stochasticity attracts endless attention.

Why it matters for physical AI

Real-world tasks rarely come with dense rewards, and intrinsic motivation offers robots a way to autonomously gather diverse, informative experience for training skills and world models without hand-crafted supervision.