Play2Perfect：灵巧操作预训练对精密装配的关键因素
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
AI总结：
提出Play2Perfect框架，通过任务无关的玩耍预训练获取可复用的操作先验，再微调用于精密装配，在稀疏奖励和接触密集任务中实现33倍样本效率提升。
摘要：
多指机器人有望实现人类手部的速度和灵巧性，但精密装配等挑战性问题仍难以解决。这些任务接触密集，使得模仿学习的数据收集困难，且奖励稀疏，使得直接使用强化学习进行探索不可行。因此，先前的工作通过使用专用夹爪、工具附件和环境夹具来结构化问题取得了进展。在这项工作中，我们认为，在机器人能够完美执行精密装配之前，它必须首先学会玩耍。我们进一步提出疑问：在玩耍学习过程中，哪些因素对精密装配至关重要？我们提出了Play2Perfect，一个通过在不同物体和目标上玩耍进行任务无关预训练的强化学习框架，然后将其微调用于精密装配。玩耍的目标是获取可复用的操作先验，例如抓取、手内重定向和姿态到达。微调则将这些通用先验适应于装配任务，将探索集中在成功所需的关键接触密集、高精度交互上。我们系统研究了玩耍预训练中的关键设计选择，包括物体多样性、训练目标、轨迹多样性和目标精度。我们表明，即使提供了密集的多阶段奖励，我们的先验也比从头开始的强化学习训练样本效率高33倍。我们展示了零样本的仿真到现实迁移，在仅0.5毫米接触间隙的紧密插入任务中实现了60%的成功率，并在长时多部件装配和拧螺丝任务中实现了超过50%的成功率。
英文摘要：
Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach. These tasks are contact-rich, making data collection for imitation learning difficult, and sparse-reward, making direct exploration with reinforcement learning (RL) intractable. Consequently, prior work has made progress by structuring the problem with specialized grippers, tool attachments, and environment fixtures. In this work, we argue that before a robot can perfect precise assembly, it must first learn to play. We further ask the question: what factors in the process of learning to play matter for precise assembly? We propose Play2Perfect, an RL framework for task-agnostic pretraining through play on diverse objects and goals, which is then perfected on precise assembly. The goal of play is to acquire reusable manipulation priors, such as grasping, in-hand reorientation and pose reaching. Finetuning then adapts this general prior to assembly, focusing exploration on the final contact-rich, high-precision interactions needed for success. We systematically study key design choices in play pretraining, including object diversity, training objective, trajectory diversity, and goal precision. We show that our prior is 33x more sample-efficient than RL training from scratch, even when provided with dense, multi-stage rewards. We demonstrate zero-shot sim-to-real transfer, achieving 60% success on tight insertions with only 0.5 mm contact clearance, and over 50% success on long-horizon multi-part assembly and screwing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
