Robotic Grasping
This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.
Papers
Showing 1–10 of 246 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | grasp_det_seg_cnn (rgb only, IW split) | 5 fold cross validation | 98.2 | — | Unverified |
| 2 | GR-ConvNet | 5 fold cross validation | 97.7 | — | Unverified |
| 3 | ResNet50 multi-grasp predictor | 5 fold cross validation | 96 | — | Unverified |
| 4 | Multi-Modal Grasp Predictor | 5 fold cross validation | 89.21 | — | Unverified |
| 5 | AlexNet, MultiGrasp | 5 fold cross validation | 88 | — | Unverified |
| 6 | GGCNN | 5 fold cross validation | 73 | — | Unverified |
| 7 | Fast Search | 5 fold cross validation | 60.5 | — | Unverified |