Zero-shot Sim-to-Real Transfer with Modular Priors
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Current end-to-end Reinforcement Learning (RL) approaches are severely limited by restrictively large search spaces and are prone to overfitting to their training environment. This is because in end-to-end RL perception, decision-making and low-level control are all being learned jointly from very sparse reward signals, with little capability of incorporating prior knowledge or existing algorithms. In this work, we propose a novel framework that effectively decouples RL for high-level decision making from low-level perception and control. This allows us to transfer a learned policy from a highly abstract simulation to a real robot without requiring any transfer learning. We therefore coin our approach zero-shot sim-to-real transfer. We successfully demonstrate our approach on the robot manipulation task of object sorting. A key component of our approach is a deep sets encoder that enables us to reinforcement learn the high-level policy based on the variable-length output of a pre-trained object detector, instead of learning from raw pixels. We show that this method can learn effective policies within mere minutes of highly simplified simulation. The learned policies can be directly deployed on a robot without further training, and generalize to variations of the task unseen during training.
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