Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios

Castri, Luca, Mghames, Sariah, Hanheide, Marc and Bellotto, Nicola (2023) Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios. In: Conference on Causal Learning and Reasoning (CLeaR), 11-14 April 2023, Tübingen, Germany.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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 the its main features and neglecting those deemed unnecessary for understanding the evolution of the system. We first validate the method on a toy problem, for which the ground-truth model is 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.

Keywords:causal discovery, feature selection, time-series, transfer entropy, causal robotics
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:53113
Deposited On:24 Jan 2023 15:26

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