SpatialPoint: Spatial-aware Point Prediction for Embodied Localization
Summary: arXiv:2603.26690v1 Announce Type: cross
Embodied intelligence fundamentally requires a capability to determine where to act in 3D space. In a groundbreaking study, researchers have formalized this requirement as embodied localization—the problem of predicting executable 3D points conditioned on visual observations and language instructions.
The concept of embodied localization is instantiated with two complementary target types:
- Touchable Points: These are surface-grounded 3D points that enable direct physical interaction.
- Air Points: These are free-space 3D points that specify placement and navigation goals, directional constraints, or geometric relations.
Embodied localization is inherently a problem of embodied 3D spatial reasoning. However, most existing vision-language systems predominantly rely on RGB inputs. This reliance necessitates implicit geometric reconstruction, which limits cross-scene generalization. This is particularly concerning given the widespread adoption of RGB-D sensors in robotics.
To address this significant gap in the field, the researchers propose a novel framework called SpatialPoint. This spatial-aware vision-language model (VLM) integrates structured depth into its architecture, allowing for the generation of camera-frame 3D coordinates. The integration of depth data significantly enhances the model’s ability to understand and predict spatial information accurately.
Dataset and Methodology
To train and evaluate their model, the researchers constructed an extensive dataset comprising 2.6 million RGB-D samples. This dataset covers both touchable and air points, allowing for comprehensive training and testing of the model’s capabilities.
The methodology involves extensive experiments to demonstrate the effectiveness of incorporating depth information into VLMs. The results indicate a marked improvement in the performance of embodied localization tasks when depth data is utilized.
Real-World Applications
SpatialPoint has been validated through real-robot deployment across three representative tasks:
- Language-guided Robotic Arm Grasping: The model enables robotic arms to grasp objects at specified locations based on natural language instructions.
- Object Placement: The model facilitates the placement of objects to target destinations, enhancing the robot’s ability to interact with its environment.
- Mobile Robot Navigation: SpatialPoint improves the navigation of mobile robots to goal positions, streamlining pathfinding processes.
Overall, the introduction of SpatialPoint marks a significant advancement in the field of embodied localization, offering a robust solution to the challenges posed by traditional vision-language systems. By leveraging structured depth, the framework not only enhances the accuracy of spatial reasoning but also broadens the applicability of robotic systems in real-world scenarios.