The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility, robustness, and scalability needed to operate in natural HRI scenarios which require the continuous acquisition of sensory information as well as the classification or assessment of human body patterns in real time. In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization. A particularity of these models is the use of growing self-organizing networks that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learning from temporally correlated input.

中文翻译：

---

基于深度神经网络自组织的人类行为识别与评估

对人类行为的稳健识别和评估在人机交互 (HRI) 领域至关重要。虽然最先进的动作感知模型在大规模动作数据集中显示出显着的结果，但它们大多缺乏在自然 HRI 场景中运行所需的灵活性、鲁棒性和可扩展性，这些场景需要持续获取感官信息以及实时对人体模式进行分类或评估。在本章中，我介绍了一组层次模型，用于通过使用神经网络自组织从深度图和 RGB 图像中学习和识别动作。