Details
| Original language | English |
|---|---|
| Title of host publication | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 10193-10200 |
| Number of pages | 8 |
| ISBN (electronic) | 9781665479271 |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 |
Publication series
| Name | IEEE International Conference on Intelligent Robots and Systems |
|---|---|
| Volume | 2022-October |
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
Abstract
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.
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
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IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 10193-10200 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
AU - Josifovski, Josip
AU - Malmir, Mohammadhossein
AU - Klarmann, Noah
AU - Zagar, Bare Luka
AU - Navarro-Guerrero, Nicolas
AU - Knoll, Alois
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.
AB - Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.
UR - http://www.scopus.com/inward/record.url?scp=85146318787&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981951
DO - 10.1109/IROS47612.2022.9981951
M3 - Conference contribution
AN - SCOPUS:85146318787
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 10193
EP - 10200
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Y2 - 23 October 2022 through 27 October 2022
ER -