Humanoid Robot Intelligence: NLP for Arithmetic Problem Solving and Autonomous Understanding

Abstract

Humanoid robots are increasingly expected to engage in conversations that require both linguistic understanding and structured problem solving. Arithmetic reasoning is a strong test of this capability because it demands precise interpretation of numerical expressions presented through natural language. This study develops a multi layered cognitive semantic architecture that enables humanoid robots to interpret verbal arithmetic in structions, derive symbolic representations, and compute accurate results. The approach integrates language grounding, context alignment, and self directed reasoning into a unified framework that supports autonomous understanding. Experimental analysis shows that the architecture adapts effectively to diverse linguistic forms and maintains stable performance across increasing task complexity.