Integrating Large Language Models with Robotics for Naturalistic Human–Robot Communication

Abstract

Context and Motivation

The combination of Large Language Models (LLMs) and robots can be called a paradigm shift in the area of human-robot communication. The inflexible and programmed nature of traditional robotic interfaces has not allowed human and machine communication to be free and natural. With the emergence of new state of the art LLMs like GPT-4 and PaLM 2, natural language understanding, reasoning, and contextual adaptation in robotics have become possible. It is this combination that gives robots the ability to understand human complex instructions, produce meaningful replies and act to respond in accordance to the human intent- thus mediating linguistic intelligence and embodied action.

Technological Overview

LLMs have shown a high level of competence in semantic understanding, language translation and contextual reasoning. With robotic perception and control modules, they give a common cognitive layer, which mediates the transmission of human instruction and robotic performance. This structure enables robots to reason on the high level concerning tasks, environments and social cues. An example is that with the help of neural networks, ambiguity in a command (such as tidy the room) can be interpreted through reasoning over sensory input and sequence planning to move a robot system from a passive agent to an active partner.

Human-Centered Implications

Naturalistic communication is not just restricted to the linguistic interaction; it encompasses emotional indicators, sense of context and flexibility. Empathy, uncertainty negotiation, and clarifying the intent can be simulated by the use of dialogues by LLM-empowered robots, and this is greatly beneficial in building trust and usability. Use cases in medical and educational, customer service, and industrial cooperation prove that the combination of LLM-based robots is more efficient and engaging due to human-like interaction. Nevertheless, ethical transparency and avoiding overanthropomorphization is still a challenge to be maintained.

Research Scope and Objectives.

This paper explores how, what, and how they can be used to integrate LLMs with robotic systems in order to have naturalistic communication. It studies a multimodal fusion strategy, a grounding strategy connecting language with perception and action, as well as reinforcement learning strategy to produce continuous adaptation. The paper also discusses human factors, such as trust, interpretability and usability that determine effectiveness of communication between human beings and the robots that are controlled by the LLM.

Contribution and Significance.

The study provides a multifaceted approach to the integration of LLM and robots, which summarizes the existing literature and suggests the design concepts of ethical, interpretable, and context-driven dialogue between humans and robots. It features the rising trends which include embodied AI, multimodal transformers, and simulation-to-real transfer learning, which altogether characterize the next generation of communicative, intelligent, and emotionally adaptive robots.