Symbolic anchoring is a crucial problem in the field of robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors. In cognitive-based robots, this process of processing sub-symbolic data from real-world sensors to obtain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for providing symbolic anchoring in the ROS 2 ecosystem. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time, increasing the intelligent behavior of robots. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper provides a description of the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2).

翻译：符号锚定是机器人领域的关键问题，它使机器人能够从传感器获取的感知信息中获得符号知识。在基于认知的机器人中，通过处理来自现实传感器的亚符号数据以获取符号知识这一过程仍是一个未解难题。针对这一问题，本文提出SAILOR框架，旨在ROS 2生态系统中提供符号锚定功能。SAILOR致力于随时间维持真实机器人中符号数据与感知数据之间的关联，从而增强机器人的智能行为。它采用基于深度学习的两种亚符号机器人技能（物体识别与匹配函数）实现语义世界建模方法。物体识别技能使机器人能够识别和确认环境中的物体，而匹配函数则使机器人能够判断新的感知数据是否对应于已有的符号数据。本文详细描述了所提出方法及其框架开发过程，并说明了该框架在MERLIN2（一种完全运行于ROS 2机器人的混合认知架构）中的集成实现。