Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots
This addresses the challenge of improving object organization and human-robot collaboration in domestic robotics, though it appears incremental by combining existing knowledge bases with robotics.
The paper tackles the problem of organizing detected objects for multipurpose robots by infusing commonsense knowledge to enhance context recognition, resulting in superior performance in placing objects in contextually relevant locations in simulated domestic settings.
This paper presents a system called Robo-CSK-Organizer that infuses commonsense knowledge from a classical knowledge based to enhance the context recognition capabilities of robots so as to facilitate the organization of detected objects by classifying them in a task-relevant manner. It is particularly useful in multipurpose robotics. Unlike systems relying solely on deep learning tools such as ChatGPT, the Robo-CSK-Organizer system stands out in multiple avenues as follows. It resolves ambiguities well, and maintains consistency in object placement. Moreover, it adapts to diverse task-based classifications. Furthermore, it contributes to explainable AI, hence helping to improve trust and human-robot collaboration. Controlled experiments performed in our work, simulating domestic robotics settings, make Robo-CSK-Organizer demonstrate superior performance while placing objects in contextually relevant locations. This work highlights the capacity of an AI-based system to conduct commonsense-guided decision-making in robotics closer to the thresholds of human cognition. Hence, Robo-CSK-Organizer makes positive impacts on AI and robotics.