AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and Symbolic Reasoning
This addresses robotic manipulation tasks by improving spatial reasoning, though it appears incremental as it builds on existing language model capabilities.
The paper tackles the problem of enhancing spatial reasoning for robotic manipulation in 3D Cartesian space using language models, achieving 66.67% accuracy compared to 37.5% for GPT-4o and 29.17% for Claude 3.5 Sonnet.
This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of language models for robotic manipulation in 3D Cartesian space. AlphaSpace employs a hierarchical semantics-based tokenization strategy that encodes spatial information at both coarse and fine-grained levels. Our approach represents objects with their attributes, positions, and height information through structured tokens, enabling precise spatial reasoning without relying on traditional vision-based embeddings. This approach enables LLMs to accurately manipulate objects by positioning them at specific (x, y, z) coordinates. Experimental results suggest that AlphaSpace demonstrates promising potential for improving manipulation tasks, achieving a total accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5 Sonnet. These results demonstrate the potential of structured spatial encoding for manipulation tasks and warrant further exploration.