Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866915035754790912 |
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| author | Ansalone, Filippo Maiorana, Flavio Affinita, Daniele Volpi, Flavio Bugli, Eugenio Petri, Francesco Brienza, Michele Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. |
| author_facet | Ansalone, Filippo Maiorana, Flavio Affinita, Daniele Volpi, Flavio Bugli, Eugenio Petri, Francesco Brienza, Michele Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. |
| contents | Advancing human-robot communication is crucial for autonomous systems operating in dynamic environments, where accurate real-time interpretation of human signals is essential. RoboCup provides a compelling scenario for testing these capabilities, requiring robots to understand referee gestures and whistle with minimal network reliance. Using the NAO robot platform, this study implements a two-stage pipeline for gesture recognition through keypoint extraction and classification, alongside continuous convolutional neural networks (CCNNs) for efficient whistle detection. The proposed approach enhances real-time human-robot interaction in a competitive setting like RoboCup, offering some tools to advance the development of autonomous systems capable of cooperating with humans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee Ansalone, Filippo Maiorana, Flavio Affinita, Daniele Volpi, Flavio Bugli, Eugenio Petri, Francesco Brienza, Michele Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Computer Vision and Pattern Recognition Human-Computer Interaction Robotics Advancing human-robot communication is crucial for autonomous systems operating in dynamic environments, where accurate real-time interpretation of human signals is essential. RoboCup provides a compelling scenario for testing these capabilities, requiring robots to understand referee gestures and whistle with minimal network reliance. Using the NAO robot platform, this study implements a two-stage pipeline for gesture recognition through keypoint extraction and classification, alongside continuous convolutional neural networks (CCNNs) for efficient whistle detection. The proposed approach enhances real-time human-robot interaction in a competitive setting like RoboCup, offering some tools to advance the development of autonomous systems capable of cooperating with humans. |
| title | Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2411.17347 |