Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | IEEE 20th International Conference on Automation Science and Engineering |
| Untertitel | CASE 2024 |
| Herausgeber (Verlag) | IEEE Computer Society |
| Seiten | 3957-3962 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9798350358513 |
| ISBN (Print) | 979-8-3503-5852-0 |
| Publikationsstatus | Veröffentlicht - 28 Aug. 2024 |
| Veranstaltung | 20th IEEE International Conference on Automation Science and Engineering, CASE 2024 - Bari, Italien Dauer: 28 Aug. 2024 → 1 Sept. 2024 |
Publikationsreihe
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| ISSN (Print) | 2161-8070 |
| ISSN (elektronisch) | 2161-8089 |
Abstract
In an era of rapid technological advances in microdevices and photonics, the importance of efficient automation solutions is becoming increasingly evident. In particular, the automation of assembly processes is gaining importance as a significant portion of costs is incurred in this phase. Programming robots, especially in micro-assembly, requires a high level of expertise due to the complex assembly systems and processes. With the rapid development of increasingly powerful Large Language Models (LLMs), their use for programming and controlling robots is becoming more and more prevalent. However, previous approaches have been limited to the field of service robots. In this paper, we present a framework that uses an LLM as an intuitive user interface for robot control in the field of micro-assembly. We integrate an LLM into a framework based on ROS2 (Robot Operation System 2), which enables skill-based control and programming of the micro-assembly robot. LLMs offer an intuitive access to the robot skills, thus facilitating robot control for users without knowledge of programming and robots. We demonstrate how an advanced LLM functions as an efficient interface, interpreting user instructions in context and initiating corresponding actions. Using a use case with exemplary user queries, we evaluate the performance of the implemented framework. Finally, we highlight further improvements and application possibilities.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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- BibTex
- RIS
IEEE 20th International Conference on Automation Science and Engineering: CASE 2024. IEEE Computer Society, 2024. S. 3957-3962 (IEEE International Conference on Automation Science and Engineering).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Large Language Model for Intuitive Control of Robots in Micro-Assembly
AU - Wiemann, Rolf
AU - Terei, Niklas
AU - Raatz, Annika
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/28
Y1 - 2024/8/28
N2 - In an era of rapid technological advances in microdevices and photonics, the importance of efficient automation solutions is becoming increasingly evident. In particular, the automation of assembly processes is gaining importance as a significant portion of costs is incurred in this phase. Programming robots, especially in micro-assembly, requires a high level of expertise due to the complex assembly systems and processes. With the rapid development of increasingly powerful Large Language Models (LLMs), their use for programming and controlling robots is becoming more and more prevalent. However, previous approaches have been limited to the field of service robots. In this paper, we present a framework that uses an LLM as an intuitive user interface for robot control in the field of micro-assembly. We integrate an LLM into a framework based on ROS2 (Robot Operation System 2), which enables skill-based control and programming of the micro-assembly robot. LLMs offer an intuitive access to the robot skills, thus facilitating robot control for users without knowledge of programming and robots. We demonstrate how an advanced LLM functions as an efficient interface, interpreting user instructions in context and initiating corresponding actions. Using a use case with exemplary user queries, we evaluate the performance of the implemented framework. Finally, we highlight further improvements and application possibilities.
AB - In an era of rapid technological advances in microdevices and photonics, the importance of efficient automation solutions is becoming increasingly evident. In particular, the automation of assembly processes is gaining importance as a significant portion of costs is incurred in this phase. Programming robots, especially in micro-assembly, requires a high level of expertise due to the complex assembly systems and processes. With the rapid development of increasingly powerful Large Language Models (LLMs), their use for programming and controlling robots is becoming more and more prevalent. However, previous approaches have been limited to the field of service robots. In this paper, we present a framework that uses an LLM as an intuitive user interface for robot control in the field of micro-assembly. We integrate an LLM into a framework based on ROS2 (Robot Operation System 2), which enables skill-based control and programming of the micro-assembly robot. LLMs offer an intuitive access to the robot skills, thus facilitating robot control for users without knowledge of programming and robots. We demonstrate how an advanced LLM functions as an efficient interface, interpreting user instructions in context and initiating corresponding actions. Using a use case with exemplary user queries, we evaluate the performance of the implemented framework. Finally, we highlight further improvements and application possibilities.
UR - http://www.scopus.com/inward/record.url?scp=85208226346&partnerID=8YFLogxK
U2 - 10.1109/CASE59546.2024.10711830
DO - 10.1109/CASE59546.2024.10711830
M3 - Conference contribution
AN - SCOPUS:85208226346
SN - 979-8-3503-5852-0
T3 - IEEE International Conference on Automation Science and Engineering
SP - 3957
EP - 3962
BT - IEEE 20th International Conference on Automation Science and Engineering
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Y2 - 28 August 2024 through 1 September 2024
ER -