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.
Abstract (translated)
尽管基于视觉或机器人皮肤的姿势识别在人机协作（HRC）领域中是一个活跃的研究方向，本文探索了仅依赖于机器人内置关节传感器的深度学习方法，从而无需使用外部传感器。我们评估了几种卷积神经网络（CNN）架构，并收集了两个数据集以研究数据表示和模型架构对识别准确率的影响。我们的结果显示基于频谱图的表示显著提高了准确性，而模型架构的作用较小。我们在新机器人姿态下的泛化测试中发现基于频谱图的模型表现更好。 在Franka Emika Research机器人的实施中，我们提出的STFT2DCNN和STT3DCNN两种方法，在接触检测和姿势分类任务上均达到了超过95%的准确率。这些研究结果表明了无需外部传感器的手感识别的可行性，并促进了向成本效益高、可扩展的人机协作解决方案的研究发展。
URL
https://arxiv.org/abs/2508.12435
