| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2014: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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| Outline of Final Research Achievements |
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. 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 confirm 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 confirm the suitability of the observed learned behaviors.
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