让机器人实时识别人类裁判手势和口哨，提升人机协作能力。
Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee
- 分两阶段处理：先提取关键点再分类手势，结合卷积网络检测口哨。
- 在NAO机器人上实现低延迟识别，支持动态竞赛环境下的实时交互。
- 适合研究人机交互、机器人竞技系统的开发者参考。
提升自主系统在动态环境中的通信能力至关重要，需准确实时解析人类信号。RoboCup为测试该能力提供理想场景，要求机器人在极少依赖网络的情况下理解裁判手势与口哨声。本研究基于NAO机器人平台，采用两阶段流水线实现手势识别：先通过关键点提取，再进行分类；同时使用连续卷积神经网络（CCNN）实现高效口哨检测。该方法显著增强了在比赛环境中的人机实时交互能力，为开发可与人类协同的自主系统提供了实用工具。
原文摘要 · Abstract (English)
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.
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