Learning long-horizon manipulation tasks efficiently is a central challenge in robot learning from demonstration. Unlike recent endeavors that focus on directly learning the task in the action domain, we focus on inferring what the robot should achieve in the task, rather than how to do so. To this end, we represent evolving scene states using a series of graphical object relationships. We propose a demonstration segmentation and pooling approach that extracts a series of manipulation graphs and estimates distributions over object states across task phases. In contrast to prior graph-based methods that capture only partial interactions or short temporal windows, our approach captures complete object interactions spanning from the onset of control to the end of the manipulation. To improve robustness when learning from multiple demonstrations, we additionally perform object matching using pre-trained visual features. In extensive experiments, we evaluate our method's demonstration segmentation accuracy and the utility of learning from multiple demonstrations for finding a desired minimal task model. Finally, we deploy the fitted models both in simulation and on a real robot, demonstrating that the resulting task representations support reliable execution across environments.

翻译：高效学习长时程操作任务是机器人演示学习中的一个核心挑战。与近期专注于直接在动作域中学习任务的研究不同，我们聚焦于推断机器人在任务中应实现的目标，而非如何实现。为此，我们使用一系列图形化的物体关系来表示演化的场景状态。我们提出了一种演示分割与池化方法，该方法提取一系列操作图并估计任务各阶段中物体状态的分布。与先前仅捕捉部分交互或短时间窗口的基于图的方法相比，我们的方法捕捉了从控制开始到操作结束的完整物体交互。为了提高从多个演示中学习时的鲁棒性，我们还利用预训练的视觉特征进行物体匹配。在大量实验中，我们评估了所提方法的演示分割准确性，以及从多个演示中学习对于寻找期望的最小任务模型的效用。最后，我们在仿真环境和真实机器人上部署了拟合的模型，结果表明所得的任务表示支持跨环境的可靠执行。