SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Tasks

Muhammad Fadhil Ginting, Sung-Kyun Kim, David Fan, Mykel Kochenderfer, Ali Agha

Abstract:

This paper addresses the problem of object-goal
navigation in autonomous inspections in real-world environments.
Object-goal navigation is crucial to enable effective inspections in
various settings, often requiring the robot to identify the target
object within a large search space. Current object inspection
methods fall short of human efficiency because they typically
cannot bootstrap prior and common sense knowledge as humans
do. In this paper, we introduce a framework that enables robots
to use semantic knowledge from prior spatial configurations
of the environment and semantic common sense knowledge.
We propose SEEK (Semantic Reasoning for Object Inspection
Tasks) that combines semantic prior knowledge with the robot’s
observations to search for and navigate toward target objects
more efficiently. SEEK maintains two representations: a Dynamic
Scene Graph (DSG) and a Relational Semantic Network (RSN).
The RSN is a compact and practical model that estimates the
probability of finding the target object across spatial elements in
the DSG. We propose a novel probabilistic planning framework
to search for the object using relational semantic knowledge.
Our simulation analyses demonstrate that SEEK outperforms
the classical planning and Large Language Models (LLMs)-based
methods that are examined in this study in terms of efficiency
for object-goal inspection tasks. We validated our approach on
a physical legged robot in urban environments, showcasing its
practicality and effectiveness in real-world inspection scenarios.