Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.16759 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916138899734528 |
|---|---|
| author | DuFrene, Kyle Strohbehn, Luke Nave, Keegan Balasubramanian, Ravi Grimm, Cindy |
| author_facet | DuFrene, Kyle Strohbehn, Luke Nave, Keegan Balasubramanian, Ravi Grimm, Cindy |
| contents | Robotic manipulation in human environments is a challenging problem for researchers and industry alike. In particular, opening doors/drawers can be challenging for robots, as the size, shape, actuation and required force is variable. Because of this, it can be difficult to collect large real-world datasets and to benchmark different control algorithms on the same hardware. In this paper we present two automated testbeds, the Door Reset Mechanism (DORM) and Drawer Reset Mechanism (DWRM), for the purpose of real world testing and data collection. These devices are low-cost, are sensorized, operate with customized variable resistance, and come with open source software. Additionally, we provide a dataset of over 600 grasps using the DORM and DWRM. We use this dataset to highlight how much variability can exist even with the same trial on the same hardware. This data can also serve as a source for real-world noise in simulation environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Door and Drawer Reset Mechanisms: Automated Mechanisms for Testing and Data Collection DuFrene, Kyle Strohbehn, Luke Nave, Keegan Balasubramanian, Ravi Grimm, Cindy Robotics Robotic manipulation in human environments is a challenging problem for researchers and industry alike. In particular, opening doors/drawers can be challenging for robots, as the size, shape, actuation and required force is variable. Because of this, it can be difficult to collect large real-world datasets and to benchmark different control algorithms on the same hardware. In this paper we present two automated testbeds, the Door Reset Mechanism (DORM) and Drawer Reset Mechanism (DWRM), for the purpose of real world testing and data collection. These devices are low-cost, are sensorized, operate with customized variable resistance, and come with open source software. Additionally, we provide a dataset of over 600 grasps using the DORM and DWRM. We use this dataset to highlight how much variability can exist even with the same trial on the same hardware. This data can also serve as a source for real-world noise in simulation environments. |
| title | The Door and Drawer Reset Mechanisms: Automated Mechanisms for Testing and Data Collection |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.16759 |