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Robotic Grasping On Cornell Grasp Dataset 1
평가 지표
5 fold cross validation
평가 결과
이 벤치마크에서 각 모델의 성능 결과
| Paper Title | ||
|---|---|---|
| grasp_det_seg_cnn (rgb only, IW split) | 98.2 | End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB |
| GR-ConvNet | 97.7 | Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network |
| ResNet50 multi-grasp predictor | 96 | Real-world multiobject, multigrasp detection |
| Multi-Modal Grasp Predictor | 89.21 | Robotic Grasp Detection using Deep Convolutional Neural Networks |
| AlexNet, MultiGrasp | 88 | Real-Time Grasp Detection Using Convolutional Neural Networks |
| GGCNN | 73 | Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach |
| Fast Search | 60.5 | Deep Learning for Detecting Robotic Grasps |
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