CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational Object Graph
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910046671077376 |
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| author | Wang, Yixuan Fermoselle, Leonor Kelestemur, Tarik Wang, Jiuguang Li, Yunzhu |
| author_facet | Wang, Yixuan Fermoselle, Leonor Kelestemur, Tarik Wang, Jiuguang Li, Yunzhu |
| contents | Mobile exploration is a longstanding challenge in robotics, yet current methods primarily focus on active perception instead of active interaction, limiting the robot's ability to interact with and fully explore its environment. Existing robotic exploration approaches via active interaction are often restricted to tabletop scenes, neglecting the unique challenges posed by mobile exploration, such as large exploration spaces, complex action spaces, and diverse object relations. In this work, we introduce a 3D relational object graph that encodes diverse object relations and enables exploration through active interaction. We develop a system based on this representation and evaluate it across diverse scenes. Our qualitative and quantitative results demonstrate the system's effectiveness and generalization across object instances, relations, and scenes, outperforming methods solely relying on vision-language models (VLMs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13338 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational Object Graph Wang, Yixuan Fermoselle, Leonor Kelestemur, Tarik Wang, Jiuguang Li, Yunzhu Robotics Computer Vision and Pattern Recognition Machine Learning Mobile exploration is a longstanding challenge in robotics, yet current methods primarily focus on active perception instead of active interaction, limiting the robot's ability to interact with and fully explore its environment. Existing robotic exploration approaches via active interaction are often restricted to tabletop scenes, neglecting the unique challenges posed by mobile exploration, such as large exploration spaces, complex action spaces, and diverse object relations. In this work, we introduce a 3D relational object graph that encodes diverse object relations and enables exploration through active interaction. We develop a system based on this representation and evaluate it across diverse scenes. Our qualitative and quantitative results demonstrate the system's effectiveness and generalization across object instances, relations, and scenes, outperforming methods solely relying on vision-language models (VLMs). |
| title | CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational Object Graph |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.13338 |