Precise robot manipulation is critical for fine-grained applications such as chemical and biological experiments, where even small errors (e.g., reagent spillage) can invalidate an entire task. Existing approaches often rely on pre-collected expert demonstrations and train policies via imitation learning (IL) or offline reinforcement learning (RL). However, obtaining high-quality demonstrations for precision tasks is difficult and time-consuming, while offline RL commonly suffers from distribution shifts and low data efficiency. We introduce a Role-Model Reinforcement Learning (RM-RL) framework that unifies online and offline training in real-world environments. The key idea is a role-model strategy that automatically generates labels for online training data using approximately optimal actions, eliminating the need for human demonstrations. RM-RL reformulates policy learning as supervised training, reducing instability from distribution mismatch and improving efficiency. A hybrid training scheme further leverages online role-model data for offline reuse, enhancing data efficiency through repeated sampling. Extensive experiments show that RM-RL converges faster and more stably than existing RL methods, yielding significant gains in real-world manipulation: 53% improvement in translation accuracy and 20% in rotation accuracy. Finally, we demonstrate the successful execution of a challenging task, precisely placing a cell plate onto a shelf, highlighting the framework's effectiveness where prior methods fail.

翻译：精确的机器人操作对于化学与生物实验等细粒度应用至关重要，其中即使微小的误差（如试剂溅洒）也可能导致整个任务失效。现有方法通常依赖于预先采集的专家示范，并通过模仿学习（IL）或离线强化学习（RL）训练策略。然而，为精确任务获取高质量的示范既困难又耗时，而离线RL常受分布偏移和数据效率低下的困扰。我们提出了一种角色模型强化学习（RM-RL）框架，该框架在真实环境中统一了在线与离线训练。其核心思想是一种角色模型策略，该策略利用近似最优动作为在线训练数据自动生成标签，从而无需人工示范。RM-RL将策略学习重新表述为监督训练，减少了因分布不匹配导致的不稳定性，并提升了效率。一种混合训练方案进一步利用在线角色模型数据进行离线复用，通过重复采样提高了数据效率。大量实验表明，RM-RL比现有RL方法收敛更快、更稳定，在真实世界操作中取得了显著提升：平移精度提高53%，旋转精度提高20%。最后，我们展示了在具有挑战性的任务——将细胞培养板精确放置到层架上的成功执行，突显了该框架在先验方法失效场景下的有效性。