mlsanityData to 14 Oct 2026

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

arXiv 2603.05574 ↗First posted 5 Mar 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 5 Mar 2026.

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References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 725
  4. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  5. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  6. 2017End-to-end Driving via Conditional Imitation Learningcited by 485
  7. 2017Overcoming Exploration in Reinforcement Learning with Demonstrationscited by 340
  8. 2019Challenges of Real-World Reinforcement Learningcited by 291
  9. 2024Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulationcited by 144
  10. 2024Policy Decorator: Model-Agnostic Online Refinement for Large Policy Modelcited by 70
  11. 2023TGRL: An Algorithm for Teacher Guided Reinforcement Learningcited by 17

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  1. 2026Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation
  2. 2025ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations
  3. 2026Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
  4. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
  5. 2025Learning a Thousand Tasks in a Day
  6. 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
  7. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperation
  8. 2025RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments