AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and Symbolic Reasoning
Alan Dao, Dinh Bach Vu, Bui Quang Huy
2025-03-25
Summary
This paper is about making robots better at following instructions and moving things around in a 3D space.
What's the problem?
AI models struggle to understand how to precisely move objects to specific locations in 3D space.
What's the solution?
The researchers developed a new method called AlphaSpace that uses special codes to represent the height of objects and helps the AI reason about how to position them accurately.
Why it matters?
This work matters because it can lead to robots that are more capable of performing complex tasks in the real world, such as assembling products or assisting in surgery.
Abstract
This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of large language models (LLMs) for 3D Cartesian space navigation. AlphaSpace employs a semantics-based tokenization strategy, encoding height information through specialized semantic tokens, and integrates primarily symbolic synthetic reasoning data. This approach enables LLMs to accurately manipulate objects by positioning them at specific [x, y, z] coordinates. Experimental results demonstrate that AlphaSpace significantly outperforms existing models on manipulation subtasks, achieving a total accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5 Sonnet.