Generative AI and Robotics: From Large Language Models to Intelligent Human-Robot Interaction and Task Planning

Authors: Ruchik Kashyapkumar Thaker

DOI: https://doi.org/10.5281/zenodo.14001479

Short DOI: https://doi.org/g8n8x3

Country: Canada

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Abstract: The rapid advancement of ChatGPT and other large language models (LLMs) has generated substantial interest in their application within robotics. This review explores the integration of generative AI into robotics, focusing on how LLMs like ChatGPT are enhancing robot intelligence, human-robot interaction, and task planning. LLMs enable improved natural language processing, facilitating more effective communication between humans and robots, while also contributing to robot perception, decision-making, and control across various modalities such as visual, auditory, and tactile inputs. The review examines the potential of LLMs to support seven types of robot intelligence, while addressing the exclusion of intrapersonal intelligence due to ethical concerns surrounding self-awareness and bias. Novel approaches for incorporating ChatGPT into task planning are discussed, particularly the use of state awareness to improve robot autonomy and adaptability. The role of reinforcement learning in optimizing LLM-based robotic systems is also considered, particularly in enhancing decision-making and generating realistic training data. Finally, the review outlines the challenges and future research directions for integrating generative AI into robotics, focusing on overcoming limitations such as outdated knowledge, multi-party interactions, and motion control.

Keywords: Generative AI, ChatGPT, Large Language Models (LLMs), Human-Robot Interaction, Robot Intelligence, Task Planning, Reinforcement Learning, Robot Perception

Paper Id: 231433

Published On: 2024-08-02

Published In: Volume 12, Issue 4, July-August 2024

Cite This: Generative AI and Robotics: From Large Language Models to Intelligent Human-Robot Interaction and Task Planning - Ruchik Kashyapkumar Thaker - IJIRMPS Volume 12, Issue 4, July-August 2024. DOI 10.5281/zenodo.14001479

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