Abstract
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 this https URL
Abstract (translated)
模仿学习是一种强大的工具，用于训练机器人操纵策略，使其可以从专家演示中学习，而无需手动编程或试错。然而，常见的数据收集方法，如人类监督，规模较小，因为它们需要时间和劳动密集。相反，任务和运动规划(TAMP)可以自主生成大规模的多样性演示数据。在本文中，我们表明，由TAMP指导生成的大规模数据集再加上适应这些数据的灵活Transformer模型是机器人操纵的强大范式。为此，我们介绍了一种称为OPTIMUS的新模仿学习系统，它通过模仿TAMP代理训练大规模视觉 motor Transformer policies。OPTIMUS介绍了一个用于生成TAMP数据的特定 curated 的管道，该管道可以用于训练高效的Transformer based policies。在本文中，我们深入研究了模仿TAMP所需的设计决策，并表明OPTIMUS可以处理超过70个不同物体的各种挑战性视觉操纵任务，包括远程选取任务、货架和连接对象操纵，达到70至80%的成功率。视频结果在此https URL上。
URL
https://arxiv.org/abs/2305.16309
