With the rapid advancement of large language models in semantic understanding and reasoning generation, robots are gradually becoming more intelligent in language-driven decision-making in office settings. However, they still face challenges in areas such as command ambiguity, concurrent task scheduling, and environmental adaptability. To this end, this study, drawing on the work of a national key research and development program, constructed a natural language intelligent decision-making framework that integrates large language models, thought chain reasoning, and multi-agent collaboration. This framework uses semantic reasoning to capture user intent and integrates real-time perception information to achieve efficient multi-robot scheduling. The research proposed an innovative mechanism in the method design that can automatically generate autonomous tasks from human-computer dialogue. This mechanism effectively reduces the interference caused by language uncertainty to the system operation by closely integrating language semantic parsing with task planning. It has been verified in multi-scenario datasets and real application environments, demonstrating its stability and adaptability in complex contexts. Based on this, this study developed a multi-agent reasoning assistant with environmental perception, task planning and autonomous reflection functions, enabling it to achieve unified decision-making and execution at both the virtual and physical levels, thereby significantly enhancing the flexibility and collaboration of cross-scenario task processing. This achievement not only verifies the feasibility of multimodal information fusion and heterogeneous robot collaboration, but also provides a brand-new technical path and practical value for the promotion and application of intelligent service robots in office scenarios.

Qizeng Sun. Research on a Robotic Natural Language Intelligent Decision-Making Framework Based on Large Language Models, Thinking Chain Reasoning, and Multi-Agent Collaboration. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 18-29. https://doi.org/10.38007/ML.2026.060103.

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