軽量深層学習による協働ロボットのハンドジェスチャー認識
Hand Gesture Recognition for Collaborative Robots Using Lightweight Deep Learning in Real-Time Robotic Systems
1103パラメータ・22KBの超軽量モデルで8種類のジェスチャーを93.5%の精度で認識し、UR5ロボットをROS2上でリアルタイム制御した。
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著者: Muhtadin, I Wayan Agus Darmawan, Muhammad Hilmi Rusydiansyah, I Ketut Eddy Purnama, Chastine Fatichah, Mauridhi Hery Purnomo
分類: cs.RO
原文アブストラクト
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.
