From Continual Learning to Causal Discovery in Robotics

Castri, Luca, Mghames, Sariah and Bellotto, Nicola (2023) From Continual Learning to Causal Discovery in Robotics. In: AAAI Bridge Program “Continual Causality”, 7-8 February 2023, Washington, DC, USA.

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From Continual Learning to Causal Discovery in Robotics
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models.

Keywords:robotics, causal inference, continual learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:53116
Deposited On:23 May 2023 12:47

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