PRISM：通过人类指令个性化模仿技能的细化
PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions
AI总结：
PRISM通过人类指令优化机器人操作中的模仿策略，提升任务执行的鲁棒性和数据效率。
摘要：
本文提出了PRISM：一种基于指令的模仿策略细化方法，用于机器人操作。该方法将模仿学习（IL）和强化学习（RL）框架整合到一个无缝流程中，使得通过一组用户引导的示范生成的广义任务模仿策略可以通过强化学习生成新的未见的细粒度行为。细化过程遵循Eureka范式，其中RL的奖励函数是通过初始的自然语言任务描述迭代生成的。所提出的方法在此基础上，通过在中间轨迹中添加人类反馈修正，来适应通用任务的细化IL策略到新的目标配置以及引入约束，从而实现策略的重用，进而提高数据效率。在模拟场景中的抓取和放置任务中的实验结果表明，所提出的方法优于没有人类反馈的策略，提高了部署的鲁棒性并减少了计算负担。
英文摘要：
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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