ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications

TL;DR

ROS-Causal is introduced, a ROS-based framework for onboard data collection and causal discovery in human-robot spatial interactions and an ad-hoc simulator, integrated with ROS, illustrates the approach's effectiveness.

Abstract

Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects. Modelling cause-and-effect relations through causal inference aids in predicting human behaviours and anticipating robot interventions. However, a critical challenge arises as existing causal discovery methods currently lack an implementation inside the ROS ecosystem, the standard de facto in robotics, hindering effective utilisation in robotics. To address this gap, this paper introduces ROS-Causal, a ROS-based framework for onboard data collection and causal discovery in human-robot spatial interactions. An ad-hoc simulator, integrated with ROS, illustrates the approach's effectiveness, showcasing the robot onboard generation of causal models during data collection. ROS-Causal is available on GitHub: https://github.com/lcastri/roscausal.git.

Figures (2)

• Figure 1: HRI scenario involving a TIAGo robot and a teleoperated person, created by ROS-Causal_HRISim.
• Figure 2: Causal model reconstructed by ROS-Causal