Name
Abstract | ||
|---|---|---|
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms. Grasp performance improves significantly when compared to the original smaller dataset. Data and tools can he accessed at https://sites. google.com/nvidia.com/graspdataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9560844 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 2 | 0.40 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clemens Eppner | 1 | 25 | 3.20 |
| Arsalan Mousavian | 2 | 11 | 5.27 |
| Dieter Fox | 3 | 12306 | 1289.74 |