Walking robots, such as quadruped robotic dogs, must be able to move safely through rough, often changing environments. Today, there are two main ways to program these walking, or legged, robots. The first is called model predictive control. This technique optimizes the robot's behavior but relies on accurate dynamics models, which are challenging to achieve in real-world settings and often require simplifying assumptions. The second is model-free reinforcement learning, which allows the robot to learn reliable but fixed behaviors, making them difficult to adapt after training.
