Asymmetric self-play for automatic goal discovery in robotic manipulation

Understand

We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects.

• We rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game.
• Alice is asked to propose challenging goals and Bob aims to solve them.
• We show that this method can discover highly diverse and complex goals without any human priors.