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arXiv · 2603.05574

PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions

Arnau Boix-Granell·Alberto San-Miguel-Tello·Magí Dalmau-Moreno·Néstor García
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Abstract

This paper presents PRISM: an instruction-conditioned refinement method for imitation policies in robotic manipulation. This approach bridges Imitation Learning (IL) and Reinforcement Learning (RL) frameworks into a seamless pipeline, such that an imitation policy on a broad generic task, generated from a set of user-guided demonstrations, can be refined through reinforcement to generate new unseen fine-grain behaviours. The refinement process follows the Eureka paradigm, where reward functions for RL are iteratively generated from an initial natural-language task description. Presented approach, builds on top of this mechanism to adapt a refined IL policy of a generic task to new goal configurations and the introduction of constraints by adding also human feedback correction on intermediate rollouts, enabling policy reusability and therefore data efficiency. Results for a pick-and-place task in a simulated scenario show that proposed method outperforms policies without human feedback, improving robustness on deployment and reducing computational burden.

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Arnau Boix-Granell, Alberto San-Miguel-Tello, Magí Dalmau-Moreno, Néstor García. 2026-03-05. PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions. https://arxiv.org/abs/2603.05574

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