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Real-time reinforcement learning is difficult because number of episodes is too much to complete learning within limited time in practice. On the other hand, in spite of trial-and-error learning, animals can complete learning within limited time. Conventional framework cannot explain it. In this paper, we address the pursuit problem using optical information tau and information of direction that is...
In this study, we investigate the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot. The ANN acts as a controller for radio frequency (RF)-localization behavior of a Khepera robot simulated in a 3D physics-based environment. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal...
Recently, the development costs for robot software is becoming bigger and bigger. Especially, the robots which works with humans in the home and office environment are expected to have various functions according to its user's demand. Therefore, RT (Robotics Technology) middleware developed by AIST offers the methods to develop desired robot system by combining RT components. However, various kinds...
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