Robot Learning

Risk-Aware Reinforcement Learning

Risk-aware reinforcement learning optimizes objectives sensitive to the distribution of returns rather than the expectation alone, using criteria such as conditional value-at-risk, worst-case formulations, or chance constraints on failure probability. Related machinery includes constrained MDPs with cost budgets, distributional RL that models full return distributions, and safety critics that veto high-risk actions during exploration and deployment.

Why it matters for physical AI

A policy that is excellent on average but occasionally drops the object or falls is unacceptable on hardware around people, so tail-risk objectives speak directly to deployment readiness.