Learning Temporal Task Models from Human Bimanual Demonstrations

Abstract

Learning temporal relations between actions in a bimanual manipulation task is important for capturing the constraints of actions required to achieve the task's goal. However, given several demonstrations of a bimanual manipulation task, the problem of identifying the true temporal dependencies between actions - if there are any - is very challenging due to contradictions. We propose a model-driven approach for learning temporal task models from multiple bimanual human demonstrations that represents temporal relations on two levels. First, temporal relations between sets of actions that exhibit a tight temporal coupling, and second, temporal relations between these sets of actions. We build on Allen's interval algebra as a representation to express relations between temporal intervals. Semantically defining these interval relations allows us to soften their formulation to deal with inaccuracies in real data obtained when observing humans demonstrating the task. Our temporal task models can be learned incrementally from multiple modalities, and allow us to reason about viable alternatives during task execution in case of unexpected events. We evaluated the approach quantitatively on two datasets and qualitatively on a humanoid robot. The evaluation shows how inherent properties of bimanual human manipulation tasks can be exploited to derive a model useful for the reproduction by humanoid robots.