動的環境におけるロボットセンサデータからの因果発見の強化
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios
転送エントロピーによる特徴選択を組み込んだ因果発見手法を提案し、ロボットセンサデータから主要特徴のみを抽出して因果モデルを再構築することで、精度と計算効率を向上させた。
著者: Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
分類: cs.RO
原文アブストラクト
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.
