simulation-to-real

OpenAI describes challenges with conventional RL on robotics tasks and introduces Hindsight Experience Replay (HER), a new RL algorithm that enables agents to learn from binary rewards by reframing failures as intended outcomes, combined with domain randomization for sim-to-real transfer.

Researchers explore a data generation pipeline using domain randomization and procedurally generated objects to train a deep neural network for robotic grasp planning. The proposed autoregressive model achieves >90% success on unseen objects in simulation and 80% in the real world, despite being trained only on random simulated objects.