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.

翻译：从传感器数据的时间序列中重建动态系统的准确因果模型是许多现实世界情景中的一个关键问题。 在本文中,我们根据我们的经验概述了因果分析在应用到自主机器人时遇到的实际挑战,以及持续学习~(CL)如何帮助克服这些挑战。我们提出了一种可能的办法来利用CL范式,使计算资源有限的机器人应用中的因果发现变得可行,同时利用机器人作为积极代理人,帮助提高重建因果模型的质量。