Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots

Commonsense knowledge improves robot decision-making: The system uses classical knowledge bases (structured commonsense reasoning) rather than relying solely on deep learning, which helps robots organize objects more intelligently—for example, placing items in contextually relevant locations like putting groceries in the kitchen rather than the bedroom.

Explainability builds trust in human-robot collaboration: Unlike black-box AI systems like ChatGPT, Robo-CSK-Organizer provides transparent reasoning for its decisions, making it easier for humans to understand and trust robots working alongside them in shared spaces.

Consistency and ambiguity resolution matter for practical robotics: The system maintains consistent object placement decisions and resolves ambiguities better than deep learning-only approaches, which is critical for multipurpose robots that need to work reliably across different household tasks.

Task-specific adaptation without retraining: The system can adapt to different task-based classifications without requiring complete retraining, making it more practical and efficient for robots that need to handle diverse real-world scenarios in domestic settings.

Hybrid AI approaches outperform single-method solutions: By combining commonsense knowledge with AI capabilities, this system demonstrates superior performance compared to relying on any single technology, suggesting that responsible AI development benefits from integrating multiple reasoning approaches.