ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications
This addresses a gap for robotics researchers and developers needing causal inference tools within the standard ROS framework, though it appears incremental as it adapts existing methods to a new platform.
The paper tackles the lack of causal discovery methods in the ROS ecosystem for robotics by introducing ROS-Causal, a framework for onboard data collection and causal analysis in human-robot interactions, demonstrated through an integrated simulator with availability on GitHub.
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.