Incorporating Commonsense Knowledge to Enhance Robot Perception

Abstract

Robots have been substantially employed across various avenues over the years. Yet, their application has been largely limited to controlled environments where variables are few and predictable. To address this challenge, we propose Robo-CSK-Organizer, a novel system that enhances robotic perception by integrating commonsense knowledge (CSK) for improved object organization, classification, and decision-making. By combining ConceptNet for semantic reasoning, DETIC for object identification, and BLIP for contextual analysis, Robo-CSK-Organizer achieves superior ambiguity resolution, task adaptation, and explainability compared to models without CSK. Testing in real-world robotics settings demonstrates notable gains in transparency, user trust, and error handling, making the approach valuable for advancing AI transparency and the development of versatile robotic applications in automation and engineering. Future directions of this work are comprehensively discussed.