RAIL: Representation-Aligned Imitation Learning for Student-Compatible Teacher Policies
Overview
This research presents RAIL, a novel approach to imitation learning that addresses the irreducible gap between teacher policies with privileged information and student policies using only raw inputs. By training a shared latent representation via contrastive learning during the teacher phase, RAIL ensures behaviors are reproducible from student observations without requiring reward modification or additional reinforcement learning on the student policy after training. The method demonstrates superior performance across multiple environments compared to strong baselines and direct reinforcement learning from raw observations, while also enabling zero-shot transfer to new tasks through teacher-only training.
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- arXiv Robotics — research abstractsSignalRAIL: Representation-Aligned Imitation Learning for Student-Compatible Teacher Policies
This research presents RAIL, a novel approach to imitation learning that addresses the irreducible gap between teacher policies with privileged information and student policies using only raw inputs. By training a shared latent representation via contrastive learning during the teacher phase, RAIL ensures behaviors are reproducible from student observations without requiring reward modification or additional reinforcement learning on the student policy after training. The method demonstrates superior performance across multiple environments compared to strong baselines and direct reinforcement learning from raw observations, while also enabling zero-shot transfer to new tasks through teacher-only training.
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