家庭机器人的效率驱动的自适应任务规划基于层次的项目-环境认知
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究介绍了家用机器人的自适应性任务规划方法,提高了它们执行新作业的能力. 该方法通过使项目和环境得到更好的理解来提高机器人的效率.
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
- 认知科学 认知科学
背景情况:
- 家庭机器人任务规划是复杂的,现有的方法往往由于对物品和环境特点的理解不足而失败.
- 当前的任务规划方法缺乏认知能力,以有效地处理家庭物品和环境的细微差别.
研究的目的:
- 为家庭机器人提出一种以效率为导向的适应性任务规划方法.
- 提高机器人的认知能力,以执行不熟悉的家庭服务.
主要方法:
- 开发了一个基于多个语义属性的先验知识 (MSAPK) 框架,用于对象表示.
- 创建了一个基于长期短期记忆 (LSTM) 的项目认知模型,用于属性赋值.
- 构建了一个环境认知模型,将项目与房间类型联系起来,以便有效地定位.
- 实施了一个层次的项目-环境认知策略,用于适应性任务规划.
主要成果:
- 拟议的方法显著提高了机器人完成家庭任务的成功和效率.
- 虚拟和现实世界的实验验证实了层级项目-环境认知策略的有效性.
- 机器人在执行各种家庭任务时表现出更高的灵活性和效率.
结论:
- 层次项目-环境认知方法为家庭机器人中的自适应任务规划提供了强大的解决方案.
- 这种方法提高了机器人的认知能力,导致更有能力,更高效的家用服务机器人.
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