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

arXiv: https://arxiv.org/abs/2409.18385 · HTML: https://mlsanity.com/paper/2409.18385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [Contextualized Representations Using Textual Encyclopedic Knowledge](https://mlsanity.com/paper/2004.12006/) (2020)
- [Hey Dona! Can you help me with student course registration?](https://mlsanity.com/paper/2303.13548/) (2023)
- [Semantic Task Planning for Service Robots in Open World](https://mlsanity.com/paper/2011.00621/) (2020)

## Abstract (arXiv)

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.