Abstract
Direct and natural interaction is essential for intuitive human-robot collaboration, eliminating the need for additional devices such as joysticks, tablets, or wearable sensors. In this paper, we present a lightweight deep learning-based hand gesture recognition system that enables humans to control collaborative robots naturally and efficiently. This model recognizes eight distinct hand gestures with only 1,103 parameters and a compact size of 22 KB, achieving an accuracy of 93.5%. To further optimize the model for real-world deployment on edge devices, we applied quantization and pruning using TensorFlow Lite, reducing the final model size to just 7 KB. The system was successfully implemented and tested on a Universal Robot UR5 collaborative robot within a real-time robotic framework based on ROS2. The results demonstrate that even extremely lightweight models can deliver accurate and responsive hand gesture-based control for collaborative robots, opening new possibilities for natural human-robot interaction in constrained environments.
Abstract (translated)
直接且自然的互动对于直观的人机协作至关重要，可以消除操纵杆、平板电脑或可穿戴传感器等额外设备的需求。本文提出了一种基于轻量级深度学习的手势识别系统，使人类能够自然高效地控制协作机器人。该模型仅使用1,103个参数和22KB的紧凑体积就能识别出8种不同的手部手势，并达到了93.5%的准确率。为了进一步优化模型以适应边缘设备上的实际部署，我们利用TensorFlow Lite进行了量化和裁剪处理，最终将模型大小减少到仅7KB。 该系统已在基于ROS2的实时机器人框架中成功实施并应用于Universal Robot UR5协作机器人上。结果表明，即使是极其轻量级的模型也能提供准确且响应迅速的手势控制功能，为受限环境中的自然人机交互开启了新的可能性。
URL
https://arxiv.org/abs/2507.10055
