High-level human activities often have rich temporal structures that determine the order in which atomic actions are executed. We propose the Temporal Context Graph (TCG), a temporal reasoning model that integrates probabilistic inference with Allen's interval algebra, to capture these temporal structures. TCGs are capable of modeling tasks with cyclical atomic actions and consisting of sequential and parallel temporal relations. We present Learning from Demonstration as the application domain where the use of TCGs can improve policy selection and address the problem of perceptual aliasing. Experiments validating the model are presented for learning two tasks from demonstration that involve structured human-robot interactions. The source code for this implementation is available at https://github.com/AssistiveRoboticsUNH/TCG.

There are many human-robot interaction (HRI) tasks that are highly structured and follow a certain temporal sequence. Learning such tasks from demonstrations requires understanding the underlying rules governing the interactions. This involves identifying and generalizing the key spatial and temporal features of the task and capturing the high-level relationships among them. Despite its crucial role in sequential task learning, temporal reasoning is often ignored in existing learning from demonstration (LFD) research. This paper proposes a holistic LFD framework that learns the underlying temporal structure of sequential HRI tasks. The proposed Temporal-Reasoning-based LFD (TR-LFD) framework relies on an automated spatial reasoning layer to identify and generalize relevant spatial features, and a temporal reasoning layer to analyze and learn the high-level temporal structure of a HRI task. We evaluate the performance of this framework by learning a well-explored task in HRI research: robot-mediated autism intervention. The source code for this implementation is available at https://github.com/AssistiveRoboticsUNH/TR-LFD.