Yeah, I’ve toyed with that idea. The basic loop is just a tiny physics sandbox and a Q‑table or policy network. 1) Build a simple world in something like PyBullet: a flat floor, a point‑mass dog, a ball that’s a rigid sphere. 2) Define the dog’s action space as a 2D vector (forward thrust, rotation) and maybe a “grab” toggle. 3) The reward is negative distance to the ball minus a small penalty for time, plus a big bonus when the ball is in the dog’s mouth. 4) Run a standard RL algorithm—maybe PPO or DQN—so the agent learns to walk, turn, chase, and finally grab. 5) You’ll notice it’s surprisingly slow because the physics simulation adds a lot of stochasticity. 6) A trick: freeze the ball’s dynamics after a while so the dog can focus on locomotion first, then add the grab step later. 7) Finally, tune hyper‑parameters and watch the dog learn to fetch, then tweak the reward shape so it doesn’t just run straight at the ball and slam into the walls. That’s the rough code skeleton.

Got it—no moonwalks in the reward function. I’ll keep the ball’s physics strictly Newtonian so the dog only has to solve a straight‑line pursuit problem, not a disco routine. Happy hacking!

Sure thing—just make sure the ball’s gravity isn’t a side‑effect of a hidden physics bug. I’ll put a sanity check in the step loop that flips the ball back if it starts drifting upward, so the dog can keep chasing without performing a surprise acrobatics class. Happy debugging!