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Online exploratory behavior acquisition model based on reinforcement learning

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Abstract

Discernment behavior is an exploratory behavior that supports object feature extraction and is a fundamental tool used by robots to orient themselves, operate objects, and establish knowledge. The main contribution of this paper is to propose an active perception model and analyzes the acquired motion patterns. In this study, we propose an active perception model in which a robot autonomously learns discernment behavior by interacting with multiple objects in its environment. During such interactions, the robot receives reinforcement signals according to the cluster distance of the observed data. In other words, we use a reinforcement learning approach to reward the successful recognition of objects. We apply our proposed model to a mobile robot simulation to observe its effectiveness. Results show that our proposed model effectively established intelligent strategies based on the relationship between object features and the robot’s configuration. In addition, we perform our experiments using real mobile robots and observe the suitability of the observed learned behaviors.

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Acknowledgments

This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 24700196.

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Authors and Affiliations

  1. Department of Mechanical Engineering and Intelligent Systems, Faculty of Engineering, Tohoku Gakuin University, 1-13-1 Chuo, Tagajo-shi, Miyagi, 985-8537, Japan

    Manabu Gouko

  2. Department of Mechanical Engineering, Graduate School of Engineering, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu-shi, Shizuoka, 432-8561, Japan

    Yuichi Kobayashi

  3. Department of Electrical Engineering and Computer Science, Faculty of Engineering, Iwate University, 4-3-5, Ueda, Morioka-shi, Iwate, 020-8551, Japan

    Chyon Hae Kim

Authors
  1. Manabu Gouko
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  2. Yuichi Kobayashi
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  3. Chyon Hae Kim
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Correspondence to Manabu Gouko.

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Gouko, M., Kobayashi, Y. & Kim, C.H. Online exploratory behavior acquisition model based on reinforcement learning. Appl Intell 42, 75–86 (2015). https://doi.org/10.1007/s10489-014-0567-4

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  • DOI: https://doi.org/10.1007/s10489-014-0567-4

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