Abstract
Humans can acquire new knowledge by themselves, and robots are also expected to have such ability by introducing curiosity. In previous researches, curiosity is expressed as just a judging system that decides whether the robot continues looking over a specific environment. In this paper we define curiosity as a direction in the multidimensional sensory space, and propose an action selection method based on the curiosity vector. The system estimates the relation between action and resulting sensory changes using the stored observed data. It selects an action so as to obtain the desired sensory information, based on the curiosity vector. This paper also describes experiments in which a wheeled mobile robot moves toward an unknown area and a humanoid discovers actions to obtain unexperienced sensory information.
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Morisawa, T., Mizuuchi, I. (2016). Diversive Curiosity in Robots and Action Selection Method for Obtaining Unexperienced Sensory Information. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_97
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DOI: https://doi.org/10.1007/978-3-319-08338-4_97
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