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

Rafael Hidalgo, Jesse Parron, Aparna S. Varde, Weitian Wang

TL;DR

This work presents Robo-CSK-Organizer, a system that embeds commonsense knowledge from ConceptNet into robotic object organization to achieve context-aware, task-relevant placement with improved explainability. By integrating CSK with perception modules (Detectron2) and context recognition (BLIP), the system generates transparent decision paths and reduces opaque reasoning common in purely data-driven approaches. Comparative evaluations against a ChatGPT-based baseline in domestic scenarios demonstrate Robo-CSK-Organizer's superior consistency (100% across tested pairs), adaptability to directive changes, and explicit reasoning trails, enhancing trust in human–robot collaboration. The work highlights the practical impact of structured knowledge integration for multipurpose robots, offering a roadmap toward more transparent, reliable, and efficient autonomous manipulation in real-world environments.

Abstract

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.

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

TL;DR

This work presents Robo-CSK-Organizer, a system that embeds commonsense knowledge from ConceptNet into robotic object organization to achieve context-aware, task-relevant placement with improved explainability. By integrating CSK with perception modules (Detectron2) and context recognition (BLIP), the system generates transparent decision paths and reduces opaque reasoning common in purely data-driven approaches. Comparative evaluations against a ChatGPT-based baseline in domestic scenarios demonstrate Robo-CSK-Organizer's superior consistency (100% across tested pairs), adaptability to directive changes, and explicit reasoning trails, enhancing trust in human–robot collaboration. The work highlights the practical impact of structured knowledge integration for multipurpose robots, offering a roadmap toward more transparent, reliable, and efficient autonomous manipulation in real-world environments.

Abstract

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.
Paper Structure (13 sections, 8 figures, 1 table, 1 algorithm)

This paper contains 13 sections, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction to Robo-CSK-Organizer
  2. The Robo-CSK-Organizer Approach
  3. System Demo and Evaluation
  4. Ambiguity Resolution
  5. Ensuring Consistency
  6. Task-Relevance Adaptability
  7. Explainability
  8. Discussion on Main Contributions
  9. Novel Integration of CSK
  10. Superiority in Consistency and Adaptability
  11. Advancements in Explainability
  12. Related Work
  13. Conclusions and Roadmap

Figures (8)

  • Figure 1: Graphical abstract of Robo-CSK-Organizer
  • Figure 2: System diagram of Robo-CSK-Organizer
  • Figure 3: ConceptNet-based decision pathway in Robo-CSK-Organizer. Illustration of the object categorization process for a pear, highlighting the selection of the optimal path based on 'AtLocation' edge weight and relational connections between 'food', 'apple', and 'pear'
  • Figure 4: Robo-CSK-Organizer has 100% consistency in all object-location pairs. ChatGPT baseline is not as consistent for all objects, specifically for adhesive tape, belt, sock, remote control, toothpaste, and aerosol can
  • Figure 5: In the adaptability phase, ChatGPT’s implementation does not cause any observable shifts, despite requesting it to change its context.
  • ...and 3 more figures