空间对象实体提示和推理嵌入式引用表达式
IEEE transactions on pattern analysis and machine intelligence
|October 23, 2023
概括
交叉模式知识推理 (CKR+) 框架通过使用事先的知识进行决策来增强体现导航. 这种方法在复杂,看不见的环境中显著提高了体内代理商的成功率.
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
- 计算机视觉 计算机视觉
背景情况:
- 嵌入式引用表达式 (REVERIE) 挑战代理使用高级指令在未见的环境中定位对象.
- 现有的视觉语言导航方法依赖于详细的说明,限制了以目标为导向的探索.
研究的目的:
- 提出一个新的跨模态知识推理 (CKR+) 框架,以实现高效,以目标为导向的体内导航.
- 通过结合先前的知识,在未见的环境中加强决策.
主要方法:
- 开发了一个房间对象意识 (ROA) 机制来区分房间和对象线索.
- 引入了一个基于知识的实体关系推理 (KERR+) 模块,利用知识图和预训练模型.
- 集成了一个实体提示符 (EP) 使用导航历史记录和视觉实体与CLIP.
- 设计了一个强化终端决定器 (RED) 进行最佳的停止决策.
主要成果:
- CKR+框架显著提高了导航效率和REVERIE基准的成功率.
- 关键指标,包括SPL和REVERIE成功率,分别大幅提升14.46%和13.81%.
- 在复杂的导航任务中证明了先前知识整合的有效性.
结论:
- 拟议的CKR+框架为体现引用表达式任务提供了一种优越的方法.
- 整合结构化和非结构化知识可以提高代理人的决策和导航性能.
- 这些方法为更有能力,更有效的体内人工智能代理铺平了道路.
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