Zwane, Sicelukwanda;
Cheney, Daniel;
Johnson, Curtis C;
Luo, Yicheng;
Bekiroglu, Yasemin;
Killpack, Marc D;
Deisenroth, Marc Peter;
(2024)
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials.
In:
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(pp. pp. 11388-11393).
IEEE: Abu Dhabi, United Arab Emirates.
Preview |
Text
2411.07342v2.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges and difficulties in integrating effective proprioceptive sensors. Large-scale soft robots (≈ two meters in length) have greater modeling complexity due to increased inertia and related effects of gravity. Common efforts to ease these modeling difficulties such as assuming simple kinematic and dynamics models also limit the general capabilities of soft robots and are not applicable in tasks requiring fast, dynamic motion like throwing and hammering. To overcome these challenges, we propose a data-efficient Bayesian optimization-based approach for learning control policies for dynamic tasks on a large-scale soft robot. Our approach optimizes the task objective function directly from commanded pressures, without requiring approximate kinematics or dynamics as an intermediate step. We demonstrate the effectiveness of our approach through both simulated and real-world experiments.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials |
| Event: | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Dates: | 14 Oct 2024 - 18 Oct 2024 |
| ISBN-13: | 979-8-3503-7770-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IROS58592.2024.10802122 |
| Publisher version: | https://doi.org/10.1109/IROS58592.2024.10802122 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Adaptation models; Uncertainty; Simulation; Dynamics; Training data; Propioception; Kinematics; Soft robotics; Bayes methods; Sensors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203906 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
