Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2011.13851v1
• Date: Fri, 27 Nov 2020 17:29:48 GMT
• Title: Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning
• Authors: Soheil Khatibi, Meisam Teimouri, Mahdi Rezaei
• Abstract summary: We present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation.
• Score: 0.8701566919381223
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation while keeping the ball into its viewpoint. Active vision is critical for humanoid decision-maker robots with a limited field of view. To deal with an active vision problem, several probabilistic entropy-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 learning problem 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.

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