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[2411.16537] RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics
Summary
The paper presents RoboSpatial, a dataset aimed at enhancing spatial understanding in robotics by providing 2D and 3D vision-language models with rich spatial information from real-world environments.
Why It Matters
RoboSpatial addresses a critical gap in current robotics research by offering a comprehensive dataset that facilitates improved spatial reasoning in robots. This advancement is essential for developing robots that can effectively perceive and interact with their environments, which is crucial for applications in automation and AI.
Key Takeaways
- RoboSpatial includes 1M images and 5k 3D scans for robust training.
- The dataset enhances spatial reasoning capabilities in robots.
- Models trained with RoboSpatial outperform existing baselines in key tasks.
- Focus on ego-, world-, and object-centric perspectives is crucial for spatial reasoning.
- The dataset is applicable for both 2D and 3D vision-language models.
Computer Science > Computer Vision and Pattern Recognition arXiv:2411.16537 (cs) [Submitted on 25 Nov 2024 (v1), last revised 18 Feb 2026 (this version, v5)] Title:RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics Authors:Chan Hee Song, Valts Blukis, Jonathan Tremblay, Stephen Tyree, Yu Su, Stan Birchfield View a PDF of the paper titled RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics, by Chan Hee Song and 5 other authors View PDF HTML (experimental) Abstract:Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with it meaningfully. In modern robotics, these capabilities are increasingly provided by vision-language models. However, these models face significant challenges in spatial reasoning tasks, as their training data are based on general-purpose image datasets that often lack sophisticated spatial understanding. For example, datasets frequently do not capture reference frame comprehension, yet effective spatial reasoning requires understanding whether to reason from ego-, world-, or object-centric perspectives. To address this issue, we introduce RoboSpatial, a large-scale dataset for spatial understanding in robotics. It consists of real indoor and tabletop scenes, captured as 3D scans and egocentric images, and annotated with rich spatial information relevant to robotics. The dataset includes...
