arXiv:2606.17046v1 Announce Type: cross Abstract: Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors from large-scale foundation models, but they still operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation. We propose the Geometric Action Model (GAM), a language-conditioned manipulation po
The proliferation of advanced AI models and the increasing demand for robotic autonomy in complex, real-world environments necessitate more sophisticated geometric reasoning.
This research addresses a critical limitation in current robot policy learning by integrating 3D geometric reasoning, which is essential for robust and contact-rich manipulation, pushing robots closer to general-purpose capabilities.
Robot policies will move beyond 2D image analysis to incorporate explicit 3D understanding, enabling more precise interaction with the physical world and reducing the gap between simulation and reality.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics sector
- · Manufacturing sector
- · Companies relying solely on 2D vision systems for robotics
- · Traditional automaton manufacturers
Improved robot performance in tasks requiring fine motor skills and complex object interaction.
Accelerated deployment of autonomous robots in diverse, unstructured environments, including consumer applications.
Increased integration of robots into daily life and industrial processes, driven by enhanced reliability and adaptability.
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