Developing Robot Autonomy via Invariant Representations

通过不变表示开发机器人自主性

• 批准号: RGPIN-2016-04847
• 负责人: Zhang, Hong
• 金额: $ 3.35万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634342
• 关键词: Developing; Robot; Autonomy; via; Invariant

People pay revisits to places encountered in the past seemingly oblivious of the time of the day, the weather conditions or even the season when the initial visits were made. They perform this task effortlessly mostly through the use of their sense of vision to recognize routes and landmarks and make navigational decisions. Can robots do the same? How does a robot process its visual sensory data and build a representation of the environment in such a way that enables it to recognize places that have been encountered in the past independently of the environmental conditions? These questions still challenge the robotics community today, and obstacles to robot autonomy. They are at the heart of this research proposal.

人们重访过去遇到的地方，似乎忘记了一天中的时间，天气状况，甚至最初访问的季节。 他们毫不费力地完成这项任务，主要是通过使用他们的视觉来识别路线和地标，并做出导航决策。机器人也能做同样的事吗？ 机器人如何处理其视觉传感数据并构建环境表示，使其能够独立于环境条件识别过去遇到过的地方？ 这些问题仍然挑战着当今的机器人社区，也是机器人自主性的障碍。 它们是这项研究提案的核心。