论文标题
使用深度强化学习的人形足球机器人的实时主动视觉
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning
论文作者
论文摘要
在本文中，我们使用针对人形足球播放机器人的深钢筋学习方法提出了一种主动的视觉方法。所提出的方法适应性地优化了机器人的观点，以获取自定位的最有用的地标，同时将球置于其视角的同时。主动视力对于具有有限视野的人形决策者机器人至关重要。为了解决主动视力问题，以前已经提出了几种基于概率的熵方法，这些方法高度依赖于自定位模型的准确性。但是，在这项研究中，我们将问题作为情节增强学习问题提出，并采用了深入的Q学习方法来解决它。所提出的网络仅需要相机的原始图像将机器人的头部移至最佳视点。该模型在实现最佳观点方面显示了80％的成功率的非常具竞争力的率。我们在Webots模拟器中模拟的类人体机器人上实现了建议的方法。我们的评估和实验结果表明，在具有较高自我定位误差的情况下，所提出的方法在Robocup上下文中优于基于熵的方法。
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.
