论文标题
使用实时神经网络的自我学习机器人
Self learning robot using real-time neural networks
论文作者
论文摘要
随着高量，低精度计算技术的进步以及对人工智能启发式系统的应用研究，通过实时学习的神经网络进行了机器学习解决方案，对研究社区也引起了极大的兴趣。本文涉及对带有手臂的机器人上实现的神经网络的研究，开发和实验分析，通过该网络会通过该网络学习直线或根据需要走路。神经网络使用梯度下降和反向传播的算法学习。神经网络的实施和培训均在Raspberry Pi 3上的机器人本地进行，因此其学习过程是完全独立的。首先在MATLAB上开发的自定义模拟器上测试神经网络，然后在Raspberry计算机上实现。存储了每一代发展网络的数据，并且在数据上进行了数学和图形分析。分析了学习率和误差率对学习过程和最终产出的因素的影响。
With the advancements in high volume, low precision computational technology and applied research on cognitive artificially intelligent heuristic systems, machine learning solutions through neural networks with real-time learning has seen an immense interest in the research community as well the industry. This paper involves research, development and experimental analysis of a neural network implemented on a robot with an arm through which evolves to learn to walk in a straight line or as required. The neural network learns using the algorithms of Gradient Descent and Backpropagation. Both the implementation and training of the neural network is done locally on the robot on a raspberry pi 3 so that its learning process is completely independent. The neural network is first tested on a custom simulator developed on MATLAB and then implemented on the raspberry computer. Data at each generation of the evolving network is stored, and analysis both mathematical and graphical is done on the data. Impact of factors like the learning rate and error tolerance on the learning process and final output is analyzed.
