Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios

Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:243-258, 2023.

Abstract

Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discovery method, PCMCI, embedding an additional feature-selection module based on transfer entropy. Starting from a prefixed set of variables, the new algorithm reconstructs the causal model of the observed system by considering only its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validate the method on a toy problem and on synthetic data of brain network, for which the ground-truth models are available, and then on a real-world robotics scenario using a large-scale time-series dataset of human trajectories. The experiments demonstrate that our solution outperforms the previous state-of-the-art technique in terms of accuracy and computational efficiency, allowing better and faster causal discovery of meaningful models from robot sensor data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v213-castri23a, title = {Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios}, author = {Castri, Luca and Mghames, Sariah and Hanheide, Marc and Bellotto, Nicola}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {243--258}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of Machine Learning Research}, month = {11--14 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v213/castri23a/castri23a.pdf}, url = {https://proceedings.mlr.press/v213/castri23a.html}, abstract = {Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discovery method, PCMCI, embedding an additional feature-selection module based on transfer entropy. Starting from a prefixed set of variables, the new algorithm reconstructs the causal model of the observed system by considering only its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validate the method on a toy problem and on synthetic data of brain network, for which the ground-truth models are available, and then on a real-world robotics scenario using a large-scale time-series dataset of human trajectories. The experiments demonstrate that our solution outperforms the previous state-of-the-art technique in terms of accuracy and computational efficiency, allowing better and faster causal discovery of meaningful models from robot sensor data.} }
Endnote
%0 Conference Paper %T Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios %A Luca Castri %A Sariah Mghames %A Marc Hanheide %A Nicola Bellotto %B Proceedings of the Second Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2023 %E Mihaela van der Schaar %E Cheng Zhang %E Dominik Janzing %F pmlr-v213-castri23a %I PMLR %P 243--258 %U https://proceedings.mlr.press/v213/castri23a.html %V 213 %X Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discovery method, PCMCI, embedding an additional feature-selection module based on transfer entropy. Starting from a prefixed set of variables, the new algorithm reconstructs the causal model of the observed system by considering only its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validate the method on a toy problem and on synthetic data of brain network, for which the ground-truth models are available, and then on a real-world robotics scenario using a large-scale time-series dataset of human trajectories. The experiments demonstrate that our solution outperforms the previous state-of-the-art technique in terms of accuracy and computational efficiency, allowing better and faster causal discovery of meaningful models from robot sensor data.
APA
Castri, L., Mghames, S., Hanheide, M. & Bellotto, N.. (2023). Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios. Proceedings of the Second Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 213:243-258 Available from https://proceedings.mlr.press/v213/castri23a.html.

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