LLM-Based Zero-Code Control for Robotic Arms: Representative Methods, Challenges, and Future Trends
Main Article Content
Keywords
Large Language Models (LLMs), robotic arms, zero-code control, natural language interaction, embodied AI
Abstract
With the rapid evolution of Large Language Models (LLMs), significant advancements have emerged in natural language processing, cross-modal reasoning, and complex task planning, heralding a paradigm shift in robotic manipulation. Traditional robotic arm control demands specialized programming and extensive engineering expertise, creating a high barrier to entry. This paper systematically reviews recent advancements in LLM-based zero-code implementation technologies for robotic arms, which enable direct control through natural language and substantially reduce development costs and operational complexity. We establish a clear technical classification framework, analyzing the performance characteristics and applicability of various methods such as direct mapping approaches (SayCan, Code as Policies, ProgPrompt) and feedback-driven architectures (Inner Monologue). We also explore multimodal fusion techniques (RT-2, PaLM-E, VIMA, VoxPoser) and their application across diverse scenarios including education, home services, and manufacturing. Furthermore, we identify key challenges—including natural language parsing accuracy, robustness of multimodal fusion, long-horizon planning, and safety—and propose future research directions aimed at fostering the integration of embodied intelligence with natural language interaction. This study clarifies the research trajectory in this field, offering theoretical support for the broader popularization and deployment of robotic technology.
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