arXiv Robotics — research abstracts· Meraj Mammadov, Pedro Zuidberg Dos Martires, Johannes Andreas Stork·2026-11-10 05:00 UTC· 2 days agoSignalEditorial score75

RAIL: Representation-Aligned Imitation Learning for Student-Compatible Teacher Policies

Summary

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.

Editorial context

The paper introduces RAIL, a method that aligns teacher and student observation spaces via contrastive learning to reduce the imitation gap without reward shaping or post-hoc fine-tuning.

Source: arXiv Robotics — research abstracts · Read original article ↗

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AIL outperforms strong baselines without reward modification or post-hoc student fine-tuning, and surpasses direct reinforcement learning from raw observations. The learned representations further enable zero-shot transfer to new tasks through teacher-only training. Project site: https://merajmammadov.com/rail/.
Open source S5

Source：arXiv Robotics — research abstracts · arxiv.org

#Other #Imitation learning #Reinforcement learning