Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910460464332800 |
|---|---|
| author | Wang, Jonathan Huang, Huang Lim, Vincent Zhang, Harry Ichnowski, Jeffrey Seita, Daniel Chen, Yunliang Goldberg, Ken |
| author_facet | Wang, Jonathan Huang, Huang Lim, Vincent Zhang, Harry Ichnowski, Jeffrey Seita, Daniel Chen, Yunliang Goldberg, Ken |
| contents | Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic manipulation of free-end cables, focusing on the problem of getting the cable endpoint to a designated target position, which may lie outside the reachable workspace of the robot end effector. We present a simulator, tune it to closely match experiments with physical cables, and then collect training data for learning dynamic cable manipulation. We evaluate with 3 cables and a physical UR5 robot. Results over 32x5 trials on 3 cables suggest that a physical UR5 robot can attain a median error distance ranging from 22% to 35% of the cable length among cables, outperforming an analytic baseline by 21% and a Gaussian Process baseline by 7% with lower interquartile range (IQR). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09581 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables Wang, Jonathan Huang, Huang Lim, Vincent Zhang, Harry Ichnowski, Jeffrey Seita, Daniel Chen, Yunliang Goldberg, Ken Robotics Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic manipulation of free-end cables, focusing on the problem of getting the cable endpoint to a designated target position, which may lie outside the reachable workspace of the robot end effector. We present a simulator, tune it to closely match experiments with physical cables, and then collect training data for learning dynamic cable manipulation. We evaluate with 3 cables and a physical UR5 robot. Results over 32x5 trials on 3 cables suggest that a physical UR5 robot can attain a median error distance ranging from 22% to 35% of the cable length among cables, outperforming an analytic baseline by 21% and a Gaussian Process baseline by 7% with lower interquartile range (IQR). |
| title | Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.09581 |