Reinforcement Learning for Robotics: Simulation to Real-World Deployment (MuJoCo + Gymnasium)
From CAD to real hardware, this video maps the full reinforcement learning pipeline for robotics: MuJoCo modeling, Gymnasium environments, PPO training, and ONNX deployment. Using a rotary inverted pendulum as a teachable example, it shows why reward shaping—not the algorithm—usually breaks sim-to-real transfer, and why domain randomization and hardware safety limits are non-negotiable for humanoid and quadruped robotics.
