A humanoid robot’s development requires an incredible combination of interdisciplinary work from engineering to mathematics, software, and machine learning. NAO is a humanoid bipedal robot designed to participate in football competitions against humans by 2050, and speed is crucial for football sports. Therefore, the focus of the paper is on improving NAO speed. This paper is aimed at testing the hypothesis of whether the humanoid NAO walking speed can be improved without changing its physical configuration. The applied research method compares three classification techniques: artificial neural network (ANN), Naïve Bayes, and decision tree to measure and predict NAO’s best walking speed, then select the best method, and enhance it to find the optimal average velocity speed. According to Aldebaran documentation, the real NAO’s robot default walking speed is 9.52 cm/s. The proposed work was initiated by studying NAO hardware platform limitations and selecting Nao’s gait 12 parameters to measure the accuracy metrics implemented in the three classification models design. Five experiments were designed to model and trace the changes for the 12 parameters. The preliminary NAO’s walking datasets open-source available at GitHub, the NAL, and RoboCup datasheets are implemented. All generated gaits’ parameters for both legs and feet in the experiments were recorded using the Choregraphe software. This dataset was divided into 30% for training and 70% for testing each model. The recorded gaits’ parameters were then fed to the three classification models to measure and predict NAO’s walking best speed. After 500 training cycles for the Naïve Bayes, the decision tree, and ANN, the RapidMiner scored 48.20%, 49.87%, and 55.12%, walking metric speed rate, respectively. Next, the emphasis was on enhancing the ANN model to reach the optimal average velocity walking speed for the real NAO. With 12 attributes, the maximum accuracy metric rate of 65.31% was reached with only four hidden layers in 500 training cycles with a 0.5 learning rate for the best walking learning process, and the ANN model predicted the optimal average velocity speed of 51.08% without stiffness: , , and . Thus, the tested hypothesis holds with the ANN model scoring the highest accuracy rate for predicting NAO’s robot walking state speed by taking both legs to gauge joint 12 parameter values.

中文翻译：

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一种改善人形 NAO 步态运动的机器学习方法

人形机器人的开发需要从工程到数学、软件和机器学习的跨学科工作的难以置信的结合。NAO 是一款人形双足机器人，旨在到 2050 年参加与人类的足球比赛，速度对足球运动至关重要。因此，本文的重点是提高 NAO 速度。本文旨在测试是否可以在不改变其物理配置的情况下提高类人 NAO 步行速度的假设。应用研究方法比较人工神经网络（ANN）、朴素贝叶斯和决策树三种分类技术来测量和预测NAO的最佳步行速度，然后选择最佳方法，并对其进行增强以找到最佳平均速度。根据毕宿五的文件，真正的 NAO 的机器人默认行走速度是 9.52 cm/s。拟议的工作是通过研究 NAO 硬件平台限制并选择 Nao 的步态 12 参数来衡量在三个分类模型设计中实现的准确度指标而启动的。设计了五个实验来模拟和跟踪 12 个参数的变化。初步 NAO 的步行数据集在 GitHub、NAL 和 RoboCup 数据表上开源。使用 Choregraphe 软件记录实验中所有生成的腿和脚的步态参数。该数据集分为 30% 用于训练和 70% 用于测试每个模型。然后将记录的步态参数输入三个分类模型，以测量和预测 NAO 的最佳步行速度。在朴素贝叶斯的 500 个训练周期之后，决策树和人工神经网络，RapidMiner 的步行指标速度分别为 48.20%、49.87% 和 55.12%。接下来，重点是增强 ANN 模型以达到真实 NAO 的最佳平均步行速度。使用 12 个属性，在 500 个训练周期中仅使用 4 个隐藏层达到 65.31% 的最大准确率指标，最佳步行学习过程的学习率为 0.5，并且 ANN 模型在没有刚度的情况下预测了 51.08% 的最佳平均速度：, ，和. 因此，经检验的假设与 ANN 模型一致，通过用双腿测量关节 12 参数值，预测 NAO 机器人行走状态速度的准确率最高。