常識知識で物体を整理するマルチパーパスロボットシステム
Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots
古典的な知識ベースの常識知識をロボットに組み込み、検出した物体をタスクに応じて分類・配置するシステムを提案し、家庭環境の模擬実験で有効性を示した。
著者: Rafael Hidalgo, Jesse Parron, Aparna S. Varde, Weitian Wang
分類: cs.RO, cs.AI
原文アブストラクト
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.