轻量模型实现93.5%准确率的手势控制，可在边缘设备实时运行。
Hand Gesture Recognition for Collaborative Robots Using Lightweight Deep Learning in Real-Time Robotic Systems
- 仅用1103参数和22KB大小的轻量模型识别8类手势。
- 量化剪枝后模型缩至7KB，准确率仍保持93.5%。
- 已在UR5机器人上实测，适合资源受限场景的自然交互。
直接自然的交互对直观的人机协作至关重要，无需额外设备如操纵杆、平板或可穿戴传感器。本文提出一种基于轻量级深度学习的手势识别系统，使人类能自然高效地控制协作机器人。该模型识别8种不同手势，仅需1,103个参数，模型大小为22 KB，准确率达93.5%。为进一步优化模型在边缘设备上的部署，采用TensorFlow Lite进行量化与剪枝，最终模型大小降至7 KB。系统在基于ROS2的实时机器人框架中成功集成并测试于Universal Robot UR5协作机器人。结果表明，即使极轻量模型也能实现准确且响应迅速的手势控制，为资源受限环境下的自然人机交互开辟新可能。
原文摘要 · Abstract (English)
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.
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