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.

翻译：在机器人动作识别领域，利用视觉系统在噪声环境中区分空间邻近但不同的臂部运动构成重大挑战。本文采用机器学习技术研究噪声环境下的机器人臂动作识别问题。具体而言，通过视觉系统追踪机器人运动轨迹，进而采用深度学习模型提取臂部关键点。通过机器学习方法的对比分析，评估该模型在噪声环境中的有效性和鲁棒性。以3×3网格环境中的井字棋游戏为案例研究，重点在于准确识别机械臂在该受限环境中选择特定位置的动作。实验结果表明，尽管数据集中加入了噪声与不确定性因素，我们的方法仍能实现精确的关键点检测与动作分类。