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Geometric Abstraction Reduces Multimodal Queries in Agentic Robot Policies
This paper proposes replacing step-by-step visual reasoning in multimodal robot policies with a 3D geometric abstraction that converts scene geometry into typed, evidence-aware symbolic references grounded from RGB-D data. The system refreshes metric values locally and returns compact textual checkpoints after each action, invoking direct visual reasoning only on demand. Experiments show improved multi-step manipulation completion while substantially reducing repeated multimodal queries.
Why it matters: Offers a concrete method to improve efficiency and reliability of agentic robot policies by using geometric abstraction, relevant to AI-driven robotics.
robot manipulationmultimodal language modelsgeometric reasoning