Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sample efficiency, enabling transferable robotic skills. Such approaches model tasks as a sequence of object poses over time. In this work, we propose a scheme for transferring observed object arrangements to novel object instances by learning these arrangements on canonical class frames. We then employ this scheme to enable a simple yet effective approach for training models from as few as five demonstrations to predict arrangements of a wide range of objects including tableware, cutlery, furniture, and desk spaces. We propose a method for optimizing the learned models to enables efficient learning of tasks such as setting a table or tidying up an office with intra-category transfer, even in the presence of distractors. We present extensive experimental results in simulation and on a real robotic system for table setting which, based on human evaluations, scored 73.3% compared to a human baseline. We make the code and trained models publicly available at http://oplict.cs.uni-freiburg.de.

翻译：机器人学中，从演示中高效学习长时程任务仍是一个开放挑战。尽管已有大量研究致力于轨迹学习，但近期以物体为中心的方法重新兴起，展现出更高的样本效率，从而实现了可迁移的机器人技能。此类方法将任务建模为随时间变化的物体位姿序列。在本工作中，我们提出一种方案，通过在学习规范类别框架上的物体排列方式，将观察到的物体布局迁移到新物体实例上。随后，我们运用该方案实现了一种简单而有效的方法，仅需五次演示即可训练模型来预测包括餐具、家具和办公空间在内的多种物体的摆放布局。我们提出一种优化学习模型的方法，以实现对诸如布置餐桌或整理办公室等任务的类内迁移高效学习，即使在存在干扰物的情况下也能有效进行。我们在仿真环境和真实机器人系统上进行了大量实验，结果表明基于人类评估的餐桌布置任务得分达到人类基准的73.3%。相关代码与训练模型已在 http://oplict.cs.uni-freiburg.de 公开提供。