Understanding Human Actions through the Lens of Executable Models
For researchers in human action recognition, this work provides a neuro-symbolic method to capture action structure, improving data efficiency and interpretability over monolithic models.
The paper introduces EXACT, a domain-specific language for representing human motions as executable models, enabling zero-shot policy inference and compositional modeling. The approach improves data efficiency and captures intuitive action relationships in segmentation and anomaly detection tasks.
Human-centred systems require an understanding of human actions in the physical world. Temporally extended sequences of actions are intentional and structured, yet existing methods for recognising what actions are performed often do not attempt to capture their structure, particularly how the actions are executed. This, however, is crucial for assessing the quality of the action's execution and its differences from other actions. To capture the internal mechanics of actions, we introduce a domain-specific language EXACT that represents human motions as underspecified motion programs, interpreted as reward-generating functions for zero-shot policy inference using forward-backwards representations. By leveraging the compositional nature of EXACT motion programs, we combine individual policies into an executable neuro-symbolic model that uses program structure for compositional modelling. We evaluate the utility of the proposed pipeline for creating executable action models by analysing motion-capture data to understand human actions, for the tasks of human action segmentation and action anomaly detection. Our results show that the use of executable action models improves data efficiency and captures intuitive relationships between actions compared with monolithic, task-specific approaches.