gaoxiang12/grasp_det_seg_cnn — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2023-09-14
Train a robot to pick items from a cluttered shelf using grasp detection.
Use the pretrained weights to predict grasp points on new images without retraining.
Use this as a baseline implementation for robotic manipulation research.
Combine grasp detection with object segmentation for a picking robot in a messy scene.
| gaoxiang12/grasp_det_seg_cnn | adeliox/klein-head-swap | ats4321/ragit | |
|---|---|---|---|
| Stars | 4 | 4 | 4 |
| Language | Python | Python | Python |
| Last pushed | 2023-09-14 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 4/5 | 3/5 | 2/5 |
| Audience | researcher | designer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires downloading the OCID_grasp dataset and a CUDA GPU environment for training.
A PyTorch deep learning system that trains robots to find good grasp points on objects and identify object categories from photos at the same time.
Mainly Python. The stack also includes Python, PyTorch, CUDA.
Dormant — no commits in 2+ years (last push 2023-09-14).
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.