Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions

TL;DR

This paper applies causal discovery methods to model and predict human spatial interactions in robot-shared environments, demonstrating improved accuracy over non-causal approaches using real-world sensor data.

Contribution

It introduces the first application of advanced causal discovery algorithms to human-robot spatial interaction modeling in real-world scenarios.

Findings

Causal models accurately capture human-environment interactions.

Causal approaches outperform non-causal prediction methods.

Demonstrated utility in service robotics scenarios.

Abstract

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.