Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions
This work addresses the need for robots to understand human behaviors in service robotics scenarios, though it appears incremental as it applies existing causal discovery algorithms to new data.
The paper tackled the problem of predicting human spatial interactions in shared environments by applying causal discovery methods to model cause-and-effect relations from real-world sensor data, resulting in improved prediction accuracy compared to non-causal approaches.
Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.