Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World
Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an updated spatial state, yet existing VLMs remain limited in both capabilities. Curre
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Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an updated spatial state, yet existing VLMs remain limited in both capabilities. Curre
This research addresses a critical limitation in current VLMs regarding dynamic spatial reasoning, which is crucial for real-world agentic applications. ML engineers working on robotics, autonomous systems, or interactive AI will find this approach to learning from interaction trajectories highly relevant for developing more robust and adaptive models.