用类别通用框架提升物体摆放学习效率，5次演示即可掌握新物品布局。
Efficient Learning of Object Placement with Intra-Category Transfer
- 在类别通用坐标系中学习物体排列，实现跨实例迁移。
- 仅需5次示范，就能准确预测餐具、家具等多样物品的摆放。
- 适合需要快速适应新物体的机器人场景，如整理桌面或办公室。
长时序任务的高效示教学习仍是机器人领域的开放挑战。尽管轨迹学习已取得进展，近期以物体为中心的方法显著提升了样本效率，实现了可迁移的机器人技能。此类方法将任务建模为时间序列中的物体位姿。本文提出一种在类别通用帧上学习物体排列的迁移方案，从而将观测到的物体布局迁移到新物体实例。该方法使模型仅需5次示范即可有效预测包括餐具、刀叉、家具和办公空间在内的广泛物体布局。我们提出优化策略，支持在存在干扰物的情况下，通过类别内迁移实现高效学习，如铺桌或整理办公室。实验在仿真和真实机器人系统上进行，人类评估得分73.3%（对比人类基准），结果表明性能接近人工水平。代码与训练模型已在https://oplict.cs.uni-freiburg.de公开。
原文摘要 · Abstract (English)
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.
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