In the realm of robot action recognition, identifying distinct but spatially proximate arm movements using vision systems in noisy environments poses a significant challenge. This paper studies robot arm action recognition in noisy environments using machine learning techniques. Specifically, a vision system is used to track the robot's movements followed by a deep learning model to extract the arm's key points. Through a comparative analysis of machine learning methods, the effectiveness and robustness of this model are assessed in noisy environments. A case study was conducted using the Tic-Tac-Toe game in a 3-by-3 grid environment, where the focus is to accurately identify the actions of the arms in selecting specific locations within this constrained environment. Experimental results show that our approach can achieve precise key point detection and action classification despite the addition of noise and uncertainties to the dataset.

在嘈杂的环境中，通过使用机器学习技术和视觉系统来识别机器人手臂的动作是一个重要挑战。本文通过比较分析机器学习方法，研究了机器人手臂在嘈杂环境中的动作识别，并评估了该模型在噪声环境中的有效性和鲁棒性。通过在一个3x3网格环境中进行的井字棋案例研究，重点是准确识别手臂在这个受限环境中选择特定位置的动作。实验结果表明，尽管数据集中添加了噪声和不确定性，我们的方法可以实现精确的关键点检测和动作分类。

噪声环境中机械臂动作识别机器学习模型的鲁棒性评估