ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Project Overview
The document discusses the integration of generative AI, specifically through the ROS-LLM framework, in educational contexts to enhance robot programming for non-experts. By utilizing large language models (LLMs), the framework allows users to input natural language commands and contextual information from the Robot Operating System (ROS), making robot programming more intuitive. This approach empowers individuals without technical expertise to articulate complex tasks effectively. Additionally, the framework promotes the creation of a library of atomic actions through imitation learning and human feedback, which contributes to the adaptability and versatility of robotic systems. The applications of this technology are diverse, spanning various fields such as domestic tasks, healthcare, and construction, indicating a significant potential for improving educational methodologies and outcomes in robotics. Overall, the document highlights how generative AI can democratize access to robotics education, fostering innovation and practical skills among learners.
Key Applications
ROS-LLM framework for intuitive robot programming using natural language prompts.
Context: Robotic systems in various environments such as domestic, healthcare, and industrial applications, targeting non-expert users.
Implementation: Integration of LLMs with ROS to enable programming through natural language, allowing non-expert users to specify tasks.
Outcomes: Demonstrated capability for non-experts to program robots effectively, leading to improved task execution and adaptability.
Challenges: Dependence on expert feedback for refining action sequences and limitations in handling complex environmental variations.
Implementation Barriers
Technical Barrier
The framework's reliance on expert feedback for refining robotic actions can limit its adaptability and increase the need for human intervention. Ambiguous input from users can lead to misinterpretations and execution errors in robotic actions.
Proposed Solutions: Enhancing the system's ability to learn from human feedback and improving the robustness of LLMs to handle ambiguous instructions. Incorporating a feedback loop for clarification and developing better parsing mechanisms to differentiate between commands and examples.
Project Team
Christopher E. Mower
Researcher
Yuhui Wan
Researcher
Hongzhan Yu
Researcher
Antoine Grosnit
Researcher
Jonas Gonzalez-Billandon
Researcher
Matthieu Zimmer
Researcher
Jinlong Wang
Researcher
Xinyu Zhang
Researcher
Yao Zhao
Researcher
Anbang Zhai
Researcher
Puze Liu
Researcher
Daniel Palenicek
Researcher
Davide Tateo
Researcher
Cesar Cadena
Researcher
Marco Hutter
Researcher
Jan Peters
Researcher
Guangjian Tian
Researcher
Yuzheng Zhuang
Researcher
Kun Shao
Researcher
Xingyue Quan
Researcher
Jianye Hao
Researcher
Jun Wang
Researcher
Haitham Bou-Ammar
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Christopher E. Mower, Yuhui Wan, Hongzhan Yu, Antoine Grosnit, Jonas Gonzalez-Billandon, Matthieu Zimmer, Jinlong Wang, Xinyu Zhang, Yao Zhao, Anbang Zhai, Puze Liu, Daniel Palenicek, Davide Tateo, Cesar Cadena, Marco Hutter, Jan Peters, Guangjian Tian, Yuzheng Zhuang, Kun Shao, Xingyue Quan, Jianye Hao, Jun Wang, Haitham Bou-Ammar
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai