航海员:机器人认知架构的感知定
Miguel Á González-Santamarta1, Francisco J Rodrıguez-Lera2, Vicente Matellan-Olivera2
1Robotics Group, Department of Mechanic Engineering, Computer and Aerospace Sciences, University of León, 24006, León, Spain. mgons@unileon.es.
Scientific reports
|January 2, 2025
概括
这项研究介绍了SAILOR,这是机器人学中的象征性定框架. 航海员将机器人感知与象征性知识联系起来,解决了认知机器人的一个关键挑战.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 象征性定对于机器人将传感器数据与象征性知识联系起来至关重要.
- 将符号下传感器数据转化为符号知识仍然是认知机器人的一个开放的问题.
研究的目的:
- 介绍SAILOR,这是一个新的象征性定框架,集成到ROS 2中.
- 让机器人能够随着时间的推移保持象征和感知数据之间的联系.
主要方法:
- 航海员采用语义世界建模方法.
- 使用基于深度学习的技能:对象识别和匹配功能.
- 与MERLIN2集成,这是ROS 2机器人的混合认知架构.
主要成果:
- 航海员框架成功地将符号数据与真实机器人的感知数据联系起来.
- 通过对公共数据集和现实世界的场景进行验证,证明了有效性.
结论:
- 航海员提供了一个强大的解决方案,用于在认知机器人中的象征性定.
- 提高机器人从感官输入获取和维护象征知识的能力.
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