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

From Continual Learning to Causal Discovery in Robotics