Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee
This work addresses the problem of improving autonomous robot cooperation with humans in dynamic environments like RoboCup, though it appears incremental.
The study tackled real-time interpretation of human referee signals in RoboCup using a two-stage pipeline for gesture recognition and whistle detection on the NAO robot platform, resulting in enhanced human-robot interaction in competitive settings.
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.