Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 11368-11373 |
| Number of pages | 6 |
| ISBN (electronic) | 979-8-3503-7770-5 |
| ISBN (print) | 979-8-3503-7771-2 |
| Publication status | Published - 14 Oct 2024 |
| Event | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024 - Abu Dhabi, United Arab Emirates Duration: 14 Oct 2024 → 18 Oct 2024 |
Publication series
| Name | IEEE International Conference on Intelligent Robots and Systems |
|---|---|
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
Abstract
Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE Control (ANODEC) has been successfully used to - fully automatically and utilizing only input-output data - design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 s of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Institute of Electrical and Electronics Engineers Inc., 2024. p. 11368-11373 (IEEE International Conference on Intelligent Robots and Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information
AU - Bachhuber, Simon
AU - Pawluchin, Alexander
AU - Pal, Arka
AU - Boblan, Ivo
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/10/14
Y1 - 2024/10/14
N2 - Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE Control (ANODEC) has been successfully used to - fully automatically and utilizing only input-output data - design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 s of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time.
AB - Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE Control (ANODEC) has been successfully used to - fully automatically and utilizing only input-output data - design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 s of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time.
UR - http://www.scopus.com/inward/record.url?scp=85216480237&partnerID=8YFLogxK
U2 - 10.1109/IROS58592.2024.10801724
DO - 10.1109/IROS58592.2024.10801724
M3 - Conference contribution
AN - SCOPUS:85216480237
SN - 979-8-3503-7771-2
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 11368
EP - 11373
BT - 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024
Y2 - 14 October 2024 through 18 October 2024
ER -