SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems

TL;DR

SemRob introduces a framework that unifies semantic stream reasoning with robotic systems, enabling integration of high-dimensional sensor data with symbolic knowledge for enhanced robotic perception and interaction.

Contribution

This paper presents SemRob, a novel platform that combines semantic stream reasoning with robotic operating systems to unify high-dimensional sensor data and symbolic streams.

Findings

Framework effectively integrates neural and symbolic data streams.

Enables advanced perception and reasoning in robotic systems.

Lays groundwork for future semantic stream reasoning applications.

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.