仅用机器人关节传感器实现高精度触觉手势识别，无需外置传感器。
Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
- 用关节传感器数据生成频谱图作为输入，提升识别准确率。
- 两种方法在弗兰卡机器人上实现超95%的接触与手势识别准确率。
- 适合追求低成本、可扩展人机协作系统的研究者使用。
尽管基于视觉或机器人皮肤的手势识别在人机协作（HRC）中备受关注，本文探索了仅依赖机器人内置关节传感器的深度学习方法，无需外部传感器。我们评估了多种卷积神经网络（CNN）架构，并构建了一个数据集，研究数据表示和模型结构对识别准确率的影响。结果表明，基于频谱图的表示显著提升准确率，而模型架构的影响较小。此外，在新机器人姿态下的泛化测试中，频谱图模型表现更优。在Franka Emika Research机器人上，STFT2DCNN和STT3DCNN两种方法在接触检测和手势分类任务中均达到95%以上准确率。这些发现证明了无外置传感器触觉识别的可行性，推动了成本更低、可扩展的人机协作解决方案的发展。
原文摘要 · Abstract (English)
While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected a dataset to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representations significantly improve accuracy, while model architecture plays a smaller role. We also tested generalization to new robot poses, where spectrogram-based models performed better. Implemented on a Franka Emika Research robot, two of our methods, STFT2DCNN and STT3DCNN, achieved over 95% accuracy in contact detection and gesture classification. These findings demonstrate the feasibility of external-sensor-free tactile recognition and promote further research toward cost-effective, scalable solutions for HRC.
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