Chapter 91: RL in Robotics
Learning objectives Train a policy in simulation (e.g. robotic arm reaching or locomotion) using a standard RL algorithm (e.g. PPO or SAC). Apply domain randomization: vary physics parameters (e.g. mass, friction, motor gains) during training so the policy sees a distribution of sim environments. Attempt to deploy the policy in a real-world setting (or a different sim with “real” parameters) and evaluate the sim-to-real gap (drop in performance or need for adaptation). Explain why domain randomization can improve transfer: the policy becomes robust to parameter variation and may generalize to the real world. Relate sim-to-real and domain randomization to robot navigation and healthcare (safety-critical deployment). Concept and real-world RL ...