Papers › Real-Time Grasp Detection Using Convolutional Neural Networks
Real-Time Grasp Detection Using Convolutional Neural Networks
Joseph Redmon, Anelia Angelova
We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.
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Code
Syntology Ran 1 of 18 code samples harvested from 3 repositories linked to this paper; 17 have no recorded run. Of those that ran: 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
18 samples harvested; 1 ran; 0 honoured the contract we drafted; 17 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Robotic Grasping | Cornell Grasp Dataset | AlexNet, MultiGrasp | 5 fold cross validation | 88 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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