SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems

Abstract

Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/semantic streams with deep neural networks, to integrate high-dimensional data streams, such as video streams and LiDAR point clouds, with traditional graph or relational stream data. As such, this positioning and system paper will outline our approach to build a platform to facilitate semantic stream reasoning capabilities on a robotic operating system called SemRob. MotivationSemantic stream processing and reasoning are getting more and more attention in various application domains such as IoT, Industry IoT, and Smart Cities [18,20,21,4,26]. Among them, many recent works, e.g. [35,17], were motivated by several use cases in autonomous driving and robotics. In parallel, reasoning and knowledge-based features attracted many research in robotics such as KnowRob [2] and semantics for robotic mapping, perception, and interaction [11]. Moreover, various works, e.g [25,14], on symbolic planning for robotics have a lot of connections to semantic streams and reasoning.