Editorial: Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

Tadahiro Taniguchi, Emre Ugur, Tetsuya Ogata, Takayuki Nagai, Yiannis Demiris

Abstract: Adaptive learning and emergence of integrative cognitive system that involve not only low-level but also high-level cognitive capabilities are crucially important in robotics [1,2,3,4,5,6]. Recent advancement in machine learning methods, e.g., deep learning and hierarchical Bayesian modeling, enables us to develop cognitive systems that integrate multi-level sensory-motor and cognitive capabilities. Low-level cognitive capabilities includes sensory perception, physical control, and behavioral motion generation, while high-level cognitive capabilities include logical inference, planning, and language acquisition. To create robots that can deal with uncertainty in our daily environment, developing machine learning methods that can integrate low-level and high-level is essential. Following the successfully organized session "the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016" held in IEEE-IROS 2016 1 , we organized this research topic. We aimed to publish original papers about the state-of-the-art machine learning methods that contribute to modeling sensory-motor and cognitive capabilities in robotics.We are pleased to present 9 research articles, related to motor and behavior learning, concept formation, language acquisition, and cognitive architecture. In this section, we briefly introduce each paper.First, three papers focused on action and behavior learning. Imitation learning is an important topic related to the integration of...