Commonsense Spatial Reasoning for Visually Intelligent Agents

TL;DR

This paper introduces a robust, viewpoint-invariant framework for commonsense spatial reasoning tailored for visually intelligent robots, enabling better understanding of object relations in dynamic environments.

Contribution

The paper presents a novel, formal spatial reasoning framework that is robust to viewpoint variations and maps to English predicates, suitable for real-world robotic applications.

Findings

Framework is robust to viewpoint and orientation changes

Spatial relations are mapped to English predicates

Implementation demonstrated in a spatial database

Abstract

Service robots are expected to reliably make sense of complex, fast-changing environments. From a cognitive standpoint, they need the appropriate reasoning capabilities and background knowledge required to exhibit human-like Visual Intelligence. In particular, our prior work has shown that the ability to reason about spatial relations between objects in the world is a key requirement for the development of Visually Intelligent Agents. In this paper, we present a framework for commonsense spatial reasoning which is tailored to real-world robotic applications. Differently from prior approaches to qualitative spatial reasoning, the proposed framework is robust to variations in the robot's viewpoint and object orientation. The spatial relations in the proposed framework are also mapped to the types of commonsense predicates used to describe typical object configurations in English. In addition, we also show how this formally-defined framework can be implemented in a concrete spatial database.