PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions
arXiv:2603.05574v1 Announce Type: cross 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
arXiv:2603.05574v1 Announce Type: cross 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.
Executive Summary
The article presents PRISM, an instruction-conditioned refinement method for imitation policies in robotic manipulation. PRISM integrates Imitation Learning (IL) and Reinforcement Learning (RL) frameworks, enabling the refinement of imitation policies through reinforcement. The approach leverages human feedback and adaptability to new goal configurations and constraints. Results show PRISM outperforms policies without human feedback, improving robustness and reducing computational burden. This development has significant implications for the efficiency and effectiveness of robotic manipulation tasks. By combining the strengths of IL and RL, PRISM offers a promising solution for real-world applications requiring adaptability and data efficiency.
Key Points
- ▸ PRISM integrates IL and RL frameworks for imitation policy refinement
- ▸ Human feedback and adaptability improve policy robustness and data efficiency
- ▸ Results demonstrate PRISM's superiority over policies without human feedback
Merits
Strength
PRISM's ability to leverage human feedback and adaptability enables policy refinement and improvement, leading to better performance and data efficiency.
Adaptability
PRISM's capacity to adapt to new goal configurations and constraints facilitates its application in diverse robotic manipulation tasks.
Demerits
Limitation
PRISM's reliance on human feedback may pose limitations in scenarios where human input is scarce or unreliable.
Complexity
PRISM's integration of IL and RL frameworks may introduce complexity, potentially hindering its scalability and deployability.
Expert Commentary
The development of PRISM represents a significant advancement in the field of robotic manipulation, leveraging the strengths of IL and RL to achieve improved performance and data efficiency. While PRISM's reliance on human feedback and adaptability may pose limitations, these challenges also offer opportunities for further research and innovation. As the field continues to evolve, PRISM's adaptability and data efficiency will play crucial roles in shaping the future of robotic manipulation and human-robot interaction.
Recommendations
- ✓ Further research should focus on addressing PRISM's limitations, such as developing alternative methods for human feedback and adaptability.
- ✓ Evaluating PRISM's performance in diverse robotic manipulation tasks and environments will provide valuable insights into its scalability and deployability.