Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship between the new objects and the objects in the demonstrations. We address this by formulating imitation learning as a conditional alignment problem between graph representations of objects. Consequently, we show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations, without any prior knowledge about the object class or any further training. In our experiments, we explore and validate our design choices, and we show that our method is highly effective for few-shot learning of several real-world, everyday tasks, whilst outperforming baselines. Videos are available on our project webpage at https://www.robot-learning.uk/implicit-graph-alignment.

翻译：摘要：考虑以下问题：给定一个任务在若干个不同物体上的少量示范，机器人如何学会在未见过的新物体上执行相同任务？这具有挑战性，因为同一类别中物体的巨大多样性使得难以推断新物体与示范物体之间的任务相关关系。我们将模仿学习形式化为物体图表示之间的条件对齐问题来解决这一问题。因此，我们证明这种条件化使得上下文学习成为可能——即机器人在观察到示范后无需任何关于物体类别的先验知识或额外训练，即可立即在一组新物体上执行任务。在实验中，我们探索并验证了设计选择，证明该方法在多个真实世界日常任务的少样本学习场景中十分有效，且性能优于基线方法。视频可在项目网页 https://www.robot-learning.uk/implicit-graph-alignment 获取。