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- W3126808210 abstract "In this paper, we present an active vision method using a deep reinforcementlearning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most usefullandmarks for self-localisation while keeping the ball into its viewpoint.Active vision is critical for humanoid decision-maker robots with a limitedfield of view. To deal with an active vision problem, several probabilisticentropy-based approaches have previously been proposed which are highly dependent on the accuracy of the self-localisation model. However, in this research, we formulate the problem as an episodic reinforcement learningproblem and employ a Deep Q-learning method to solve it. The proposed network only requires the raw images of the camera to move the robot's head toward the best viewpoint. The model shows a very competitive rate of 80% success rate in achieving the best viewpoint. We implemented the proposed method on a humanoid robot simulated in Webots simulator. Our evaluations and experimental results show that the proposed method outperforms the entropy-based methods in the RoboCup context, in cases with high self-localisation errors." @default.
- W3126808210 created "2021-02-15" @default.
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- W3126808210 date "2021-01-01" @default.
- W3126808210 modified "2023-10-18" @default.
- W3126808210 title "Real-time Active Vision for a Humanoid Soccer Robot using Deep Reinforcement Learning" @default.
- W3126808210 doi "https://doi.org/10.5220/0010237307420751" @default.
- W3126808210 hasPublicationYear "2021" @default.
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