継続学習からロボティクスにおける因果発見へ
From Continual Learning to Causal Discovery in Robotics
ロボットのセンサ時系列データから因果モデルを再構築する際の実践的課題を整理し、継続学習を活用して計算資源が限られたロボットでも因果発見を可能にする方法を提案した概説論文。
著者: Luca Castri, Sariah Mghames, Nicola Bellotto
分類: cs.RO
原文アブストラクト
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.
