Bayesian network-based behavior control for Skilligent robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning of subgoals for goal-oriented behavior control of mobile robots
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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In this paper, an algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment