Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios
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| Title: | Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios |
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| Authors: | L. Castri, G. Beraldo, S. Mghames, M. Hanheide, N. Bellotto |
| Source: | 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). :1603-1609 |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | FOS: Computer and information sciences, Computer Science - Robotics, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Analytical models , Time-frequency analysis , Service robots , Predictive models , Data collection , Data models , Spatial databases , Real-time systems , Planning , Standards, Robotics (cs.RO) |
| Description: | 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 Published at 2024 IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/ro-man60168.2024.10731290 |
| DOI: | 10.48550/arxiv.2406.04955 |
| Access URL: | http://arxiv.org/abs/2406.04955 |
| Rights: | STM Policy #29 CC BY NC SA |
| Accession Number: | edsair.doi.dedup.....c8fd05a8b96511efdf5c94e4d8154f6c |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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<br />Published at 2024 IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) |
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| DOI: | 10.1109/ro-man60168.2024.10731290 |