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[2406.04955] Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios
Summary
This article presents an experimental evaluation of ROS-Causal, a framework for causal discovery in human-robot spatial interactions, demonstrating its effectiveness in both simulation and real-world scenarios.
Why It Matters
Understanding human-robot interactions is crucial for deploying robots in shared environments. This research bridges a gap in causal inference methods within the ROS ecosystem, enhancing robotic systems' performance and safety in real-world applications.
Key Takeaways
- ROS-Causal enables onboard data collection and causal discovery for robots.
- The framework was evaluated in both simulated and real-world lab scenarios.
- Causal models generated in simulations align with those from actual experiments.
- This approach enhances understanding of human behavior for better robot interventions.
- The findings support improved deployment of robots in human-shared environments.
Computer Science > Robotics arXiv:2406.04955 (cs) [Submitted on 7 Jun 2024 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios Authors:Luca Castri, Gloria Beraldo, Sariah Mghames, Marc Hanheide, Nicola Bellotto View a PDF of the paper titled Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios, by Luca Castri and 4 other authors View PDF HTML (experimental) 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 mo...
