Integrating Large Language Models with Robotics and Control Engineering

The field of robotics and control engineering is witnessing a significant shift towards the integration of large language models (LLMs) to enhance their capabilities. Researchers are exploring the potential of LLMs to generate control actions, perceive environments, and execute complex motions. This trend is expected to revolutionize the way robots interact with their surroundings and perform tasks. The use of LLMs is also being investigated for risk-aware planning, user personalization, and grounding language plans into executable skills in cluttered environments. Furthermore, LLMs are being utilized to support formal knowledge representation and enhance control engineering content with interactive semantic layers. Noteworthy papers in this area include:

  • A paper that presents a system for empowering off-the-shelf Vision-Language Models to control humanoid agents, achieving an interaction task success rate of 90.2% in open environments.
  • A paper that introduces a Multi-Agent Robotic System powered by multimodal large language models for assistive intelligence, demonstrating superior overall performance in risk-aware planning and coordinated multi-agent execution.

Sources

Endowing GPT-4 with a Humanoid Body: Building the Bridge Between Off-the-Shelf VLMs and the Physical World

Large Language Models for Control

MARS: Multi-Agent Robotic System with Multimodal Large Language Models for Assistive Intelligence

LLM-Supported Formal Knowledge Representation for Enhancing Control Engineering Content with an Interactive Semantic Layer

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