用常识知识帮机器人智能分类摆放物品，提升任务适应性。
Robo-CSK-Organizer: Commonsense Knowledge to Organize Detected Objects for Multipurpose Robots
- 结合经典知识库，让机器人根据任务需求分类物体。
- 在模拟家庭场景中表现优于纯深度学习方法。
- 决策可解释，适合需要人机协作的多功能机器人。
本文提出Robo-CSK-Organizer系统，通过引入经典知识库中的常识知识，增强机器人对上下文的理解能力，从而实现按任务相关性组织检测到的物体。该系统在多功能机器人应用中尤为有效。与依赖单一深度学习模型（如ChatGPT）的系统不同，Robo-CSK-Organizer在处理模糊情况时更具鲁棒性，能保持物体放置的一致性，并支持多样化的任务导向分类。此外，其决策过程具备可解释性，有助于提升用户信任和人机协作效率。在模拟家庭机器人场景下的受控实验表明，该系统在将物体放置于语境相关位置时表现出更优性能。本研究展示了基于AI的系统在接近人类认知水平的常识驱动决策方面的能力，对人工智能与机器人学领域具有积极影响。
原文摘要 · Abstract (English)
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.
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