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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12008 |
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| _version_ | 1866908539714273280 |
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| author | Song, Yuqing Tonola, Cesare Savazzi, Stefano Kianoush, Sanaz Pedrocchi, Nicola Sigg, Stephan |
| author_facet | Song, Yuqing Tonola, Cesare Savazzi, Stefano Kianoush, Sanaz Pedrocchi, Nicola Sigg, Stephan |
| contents | As robots become increasingly prevalent in both homes and industrial settings, the demand for intuitive and efficient human-machine interaction continues to rise. Gesture recognition offers an intuitive control method that does not require physical contact with devices and can be implemented using various sensing technologies. Wireless solutions are particularly flexible and minimally invasive. While camera-based vision systems are commonly used, they often raise privacy concerns and can struggle in complex or poorly lit environments. In contrast, radar sensing preserves privacy, is robust to occlusions and lighting, and provides rich spatial data such as distance, relative velocity, and angle. We present a gesture-controlled robotic arm using mm-wave radar for reliable, contactless motion recognition. Nine gestures are recognized and mapped to real-time commands with precision. Case studies are conducted to demonstrate the system practicality, performance and reliability for gesture-based robotic manipulation. Unlike prior work that treats gesture recognition and robotic control separately, our system unifies both into a real-time pipeline for seamless, contactless human-robot interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gesture-Based Robot Control Integrating Mm-wave Radar and Behavior Trees Song, Yuqing Tonola, Cesare Savazzi, Stefano Kianoush, Sanaz Pedrocchi, Nicola Sigg, Stephan Robotics As robots become increasingly prevalent in both homes and industrial settings, the demand for intuitive and efficient human-machine interaction continues to rise. Gesture recognition offers an intuitive control method that does not require physical contact with devices and can be implemented using various sensing technologies. Wireless solutions are particularly flexible and minimally invasive. While camera-based vision systems are commonly used, they often raise privacy concerns and can struggle in complex or poorly lit environments. In contrast, radar sensing preserves privacy, is robust to occlusions and lighting, and provides rich spatial data such as distance, relative velocity, and angle. We present a gesture-controlled robotic arm using mm-wave radar for reliable, contactless motion recognition. Nine gestures are recognized and mapped to real-time commands with precision. Case studies are conducted to demonstrate the system practicality, performance and reliability for gesture-based robotic manipulation. Unlike prior work that treats gesture recognition and robotic control separately, our system unifies both into a real-time pipeline for seamless, contactless human-robot interaction. |
| title | Gesture-Based Robot Control Integrating Mm-wave Radar and Behavior Trees |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.12008 |