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 enable 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 https://oplict.cs.uni-freiburg.de.

翻译：在机器人学中，从演示中高效学习长时程任务仍是一个开放挑战。尽管大量研究致力于轨迹学习，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，使机器人技能具备可迁移性。此类方法将任务建模为物体姿态随时间变化的序列。在本工作中，我们提出一种方案，通过学习典型类别框架上的物体排列，将观察到的物体布局迁移至新物体实例。随后，我们运用该方案实现一种简单而有效的方法，仅需五次演示即可训练模型，以预测包括餐具、家具及办公空间在内的广泛物体布局。我们提出一种优化学习模型的方法，支持在存在干扰物的情况下，通过类内迁移高效学习诸如布置餐桌或整理办公室等任务。我们在仿真和真实机器人系统上进行了大量实验，针对餐桌布置任务，基于人工评估得分为人类基准的73.3%。代码与训练模型已公开于 https://oplict.cs.uni-freiburg.de。