An Everyday Robotic System that Maintains Local Rules Using Semantic Map Based on Long-Term Episodic Memory
An Everyday Robotic System that Maintains Local Rules Using Semantic Map Based on Long-Term Episodic Memory
复制标题
使用基于长期情景记忆的语义图维护局部规则的日常机器人系统
DOI：
10.1109/iros.2018.8594481
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Inaba Masayuki
中科院分区：
文献类型：
--
作者：
Furuta Yuki;Okada Kei;Kakiuchi Yohei;Inaba Masayuki
为了使机器人能够在真实的家庭环境中工作，他们不仅要考虑全球社会的常识，还要了解那里的现有规则。由于这种“本地规则”是事先无法描述的，机器人代理必须在部署后通过他们的生活来获得它们。为了实现这一目标，我们开发了一个框架，a）让机器人在其部署的环境中记录长期情景记忆，B）自主构建概率对象定位地图作为记录数据的结构化，以及c）根据地图制定适应性任务计划。我们在PR2和Fetch机器人上配备了我们的框架，这些机器人使用环境的语义常识操作和记录了41天的情景记忆。我们还进行了演示，其中PR2机器人整理了一个房间，表明机器人代理可以成功地计划和执行本地规则感知的家庭辅助任务，通过使用我们提出的框架。
To enable robots to work on real home environments, they have to not only consider common knowledge in the global society, but also be aware of existing rules there. Since such “local rules” are not describable beforehand, robot agents must acquire them through their lives after deployment. To achieve this, we developed a framework that a) lets robots record long-term episodic memories in their deployed environments, b) autonomously builds probabilistic object localization map as structurization of logged data and c) make adapted task plans based on the map. We equipped our framework on PR2 and Fetch robots operating and recording episodic memory for 41 days with semantic common knowledge of the environment. We also conducted demonstrations in which a PR2 robot tidied up a room, showing that the robot agent can successfully plan and execute local-rule-aware home assistive tasks by using our proposed framework.
使用对话和人类观察向学习机器人助手指示任务
影响因子：
2.5
作者：
P. Rybski;J. Stolarz;Kevin Yoon;M. Veloso
通讯作者：
M. Veloso