Using large language models to enable adaptation and resilience in autonomous aerial robots

Paper Title:

REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robots

Published on:

2 November 2023

Primary Category:

Robotics

Paper Authors:

Andrea Tagliabue,

Kota Kondo,

Tong Zhao,

Mason Peterson,

Claudius T. Tewari,

Jonathan P. How

Bullets

Key Details

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REAL enables adaptation at both low-level control and high-level mission planning in aerial robots

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A large language model is queried online to choose actions based on natural language logs and error codes

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The method is demonstrated on a real multirotor, improving tracking under uncertainties

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The large language model can trigger emergency landings based on detecting issues in logs

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The initial prompt requires minimal knowledge of the robot's dynamics or mission

Explore the topics in this paper

decision making

intelligent robotics

knowledge reasoning

language model

uav

AI generated summary

Using large language models to enable adaptation and resilience in autonomous aerial robots

This paper presents REAL, a method to leverage large language models' prior knowledge and reasoning abilities to enable online adaptation and decision-making across different components of an autonomous aerial robot's software stack. REAL allows the aerial robot to improve its trajectory tracking and make mission-critical decisions by querying a large language model, requiring only a simple initial prompt and no explicit dynamics modeling.