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Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
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本文探讨基于机器人内置关节传感器的手势识别深度学习方法,通过CNN架构和数据集研究数据表示和模型架构对识别准确率的影响,实现95%以上的识别准确率,推动低成本、可扩展的HRC解决方案研究。

arXiv:2508.12435v1 Announce Type: cross Abstract: 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 two datasets 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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机器人 手势识别 深度学习 传感器 HRC
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