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.

翻译：从传感器数据的时间序列中识别动态系统的主要特征并学习其因果关系，是众多实际机器人应用中的关键问题。本文提出了一种对当前最先进的因果发现方法PCMCI的扩展，通过嵌入基于转移熵的额外特征选择模块。该新算法从预设变量集合出发，仅考虑系统的主要特征，并忽略被认为对理解系统演化不必要的特征，从而重构观测系统的因果模型。我们首先在玩具问题及脑网络合成数据上对方法进行验证（这些场景下真实模型已知），随后在大规模人类轨迹时间序列数据集的实际机器人场景中进行测试。实验表明，与先前最先进的技术相比，我们的方案在准确性和计算效率方面均表现更优，能够从机器人传感器数据中更快、更准确地发现具有意义的因果模型。