For a robot to be capable of development it must be able to explore its environment and learn from its experiences. It must find (or create) opportunities to experience the unfamiliar in ways that reveal properties valid beyond the immediate context. In this paper, we develop a novel method for using the rhythm of everyday actions as a basis for identifying the characteristic appearance and sounds associated with objects, people, and the robot itself. Our approach is to identify and segment groups of signals in individual modalities (sight, hearing, and proprioception) based on their rhythmic variation, then to identify and bind causally-related groups of signals across different modalities. By including proprioception as a modality, this cross-modal binding method applies to the robot itself, and we report a series of experiments in which the robot learns about the characteristics of its own body.
2022. Construction of multi-modal perception model of communicative robot in non-structural cyber physical system environment based on optimized BT-SVM model. Computer Communications 181 ► pp. 182 ff.
Zambelli, Martina, Antoine Cully & Yiannis Demiris
2020. Multimodal representation models for prediction and control from partial information. Robotics and Autonomous Systems 123 ► pp. 103312 ff.
Zhou, Xuefeng, Hongmin Wu, Juan Rojas, Zhihao Xu & Shuai Li
2020. Nonparametric Bayesian Modeling of Multimodal Time Series. In Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, ► pp. 11 ff.
Park, Daehyung, Zackory Erickson, Tapomayukh Bhattacharjee & Charles C. Kemp
2016. 2016 IEEE International Conference on Robotics and Automation (ICRA), ► pp. 407 ff.
Yonekura, Kenta, Chyon Hae Kim, Kazuhiro Nakadai, Hiroshi Tsujino & Kazuhito Yokoi
2015. Prevention of accomplishing synchronous multi-modal human–robot cooperation by using visual rhythms. Advanced Robotics 29:14 ► pp. 901 ff.
Arsénio, Artur, Hugo Serra, Rui Francisco, Fernando Nabais, João Andrade & Eduardo Serrano
2014. Internet of Intelligent Things: Bringing Artificial Intelligence into Things and Communication Networks. In Inter-cooperative Collective Intelligence: Techniques and Applications [Studies in Computational Intelligence, 495], ► pp. 1 ff.
van Geert, Paul
2009. Development, Complex Dynamic Systems of. In Encyclopedia of Complexity and Systems Science, ► pp. 1872 ff.
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