Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios

TL;DR

This paper evaluates the ROS-Causal framework for causal inference in human-robot interactions, demonstrating its effectiveness both in simulation and real-world scenarios to improve robot understanding and performance in shared environments.

Contribution

It presents an experimental validation of ROS-Causal in real-world and simulated human-robot interactions, enabling onboard causal discovery in the ROS ecosystem.

Findings

Causal models are consistent between simulation and real-world experiments.

ROS-Causal enables onboard causal inference during data collection.

The framework enhances understanding of human-robot spatial interactions.

Abstract

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal