Robot Learning

Reward Engineering

Reward engineering is the design and iterative tuning of reward functions so that reinforcement learning produces the intended behavior, combining task success terms with shaping bonuses, energy and smoothness penalties, and constraint costs. It is notoriously error-prone: agents exploit loopholes in misspecified rewards, a failure mode known as reward hacking, and locomotion rewards in particular often require dozens of carefully weighted terms.

Why it matters for physical AI

Reward design consumes a large share of practitioner effort in applied robot RL, and its brittleness motivates alternatives such as demonstrations, preference learning, and foundation-model-generated rewards.