Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contrast, Task and Motion Planning (TAMP) can autonomously generate large-scale datasets of diverse demonstrations. In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation. To that end, we present a novel imitation learning system called OPTIMUS that trains large-scale visuomotor Transformer policies by imitating a TAMP agent. OPTIMUS introduces a pipeline for generating TAMP data that is specifically curated for imitation learning and can be used to train performant transformer-based policies. In this paper, we present a thorough study of the design decisions required to imitate TAMP and demonstrate that OPTIMUS can solve a wide variety of challenging vision-based manipulation tasks with over 70 different objects, ranging from long-horizon pick-and-place tasks, to shelf and articulated object manipulation, achieving 70 to 80% success rates. Video results at https://mihdalal.github.io/optimus/

翻译：模仿学习是训练机器人操作策略的强大工具，能够使其从专家示范中学习，而无需手动编程或试错。然而，常见的数据收集方法（如人工监督）因耗时费力而难以规模化。相比之下，任务与运动规划（TAMP）可自动生成大规模多样化示范数据集。本文表明，TAMP监督器生成的大规模数据集与适配这些数据的灵活Transformer模型相结合，是机器人操作的强大范式。为此，我们提出一种名为OPTIMUS的新型模仿学习系统，通过模仿TAMP代理来训练大规模视觉运动Transformer策略。OPTIMUS引入了一套专为模仿学习设计的TAMP数据生成流程，可用于训练高性能的Transformer基础策略。本文系统研究了模仿TAMP所需的设计决策，并证明OPTIMUS能在包含超过70种不同物体的多种具有挑战性的视觉操作任务中（涵盖长时程抓取放置任务、货架操作及铰接物体操作）实现70%至80%的成功率。视频结果详见https://mihdalal.github.io/optimus/