Representation and Exchange of Knowledge about Actions, Objects, and Environments in the RoboEarth Framework

abstract

The community-based generation of content has been tremendously successful in the World Wide Web - people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform. Note to practitioners: In this paper, we report on a formal language for knowledge representation that is used in the ROBOEARTH system, a web-based knowledge base intended to be like a "Wikipedia for robots." The objective is to enable robots to share information about how to perform actions, how to recognize and interact with objects, and where to find objects in an environment. The developed language allows to store such information in a format that supports logical inference, so that robots can for example autonomously decide if they have all prerequisites needed for performing a described action. In laboratory experiments, the system has been applied to the exchange of pick-and-place style activities between two mobile manipulation robots. We are currently extending the representation towards more fine-grained action specifications.