Summary of Robospatial: Teaching Spatial Understanding to 2d and 3d Vision-language Models For Robotics, by Chan Hee Song et al.
RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics
by Chan Hee Song, Valts Blukis, Jonathan Tremblay, Stephen Tyree, Yu Su, Stan Birchfield
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to enhancing the spatial understanding capabilities of visual language models is presented in this research paper. The existing training data sources for these models rely heavily on general-purpose image datasets that lack sophisticated spatial scene understanding, making it challenging for them to reason about and interact meaningfully within the world. To address this issue, the authors introduce RoboSpatial, a large-scale dataset consisting of 3D scans and egocentric images, annotated with rich spatial information relevant to robotics. The dataset includes over 1M images, 5K 3D scans, and 3M annotated spatial relationships, making it both 2D and 3D ready. Experimental results show that models trained with RoboSpatial outperform baselines on downstream tasks such as spatial affordance prediction, spatial relationship prediction, and robotics manipulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robots need to understand space to make good decisions. Right now, visual language models are not very good at this because their training data is too general. They don’t have the right information to understand spatial relationships between objects. To fix this, researchers created a new dataset called RoboSpatial that contains 3D scans and pictures taken from different perspectives, all labeled with important spatial details. This allows machines to learn how to reason about space and interact with the world in a more meaningful way. |
Keywords
» Artificial intelligence » Scene understanding
