- Grasping Method
- Grasping Dataset
- Grasping Simulation
- Else Gripper | Else Arm
- Research Group
- Conference
- Talk
- Video
- Book
- Survey about Robotic Grasping
- My Ideas
@ Date: 2023.11.25
@ Author: Zhi Wang(Leo TX)
@ Email: [email protected]
📌mostly for two-fingers gripper
| Name | Features | 📄Paper | 🌐Project | 📁Github | |
|---|---|---|---|---|---|
| GPD | Detect grasp poses in point clouds | Paper | Project | Github | |
| GQ-CNN | Grasp Quality Convolutional Neural Networks | Paper | Project | Github | |
| GG-CNN | Generative Grasping CNN | Paper | Project | Github | |
| GraspNet-Baseline | Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020) | Paper | Project | Github | |
| GR-CNN | Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network | Paper | Project | Github | |
| Contact-GraspNet | Paper | Project | Github | ||
| GPNet | Paper | Project | Github | ||
| Graspness | Paper | Project | Github | ||
| Keypoint-GraspNet | Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input | Paper | Project | Github | |
| Paper | Project | Github | |||
| PointNetGPD | Paper | Project | Github | ||
| RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention | Paper | add depth in planar grasping | |||
| Scale-Balanced-Grasp | Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes | Paper | Project | Github | |
| VCPD | Volumetric-based Contact Point Detection for 7-DoF Grasping | Paper | Project | Github | |
| Paper | Project | Github | |||
| L2G | End-to-End Learning to Grasp via Sampling From Object Point Clouds | Paper | Project | Github | pc |
| SymmetryGrasp | SymmetryGrasp: Symmetry-Aware Antipodal Grasp Detection From Single-View RGB-D Images | Paper | Project | Github | pc |
| Paper | Project | Github | |||
| Paper | Project | Github | |||
| Paper | Project | Github | |||
| Paper | Project | Github | |||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| VGN | VGN is a 3D convolutional neural network for real-time 6 DOF grasp pose detection. The network accepts a Truncated Signed Distance Function (TSDF) representation of the scene and outputs a volume of the same spatial resolution, where each cell contains the predicted quality, orientation, and width of a grasp executed at the center of the voxel. The network is trained on a synthetic grasping dataset generated with physics simulation. | Paper | Project | Github |
| GIGA | Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations | Paper | Project | Github |
| GraspNeRF | GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| visual-pushing-grasping | Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning | Paper | Project | Github |
| drl_grasping | Deep Reinforcement Learning for Robotic Grasping from Octrees | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| KOSMOS-E | Paper | Project | Github | |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| Dex-Net | Dexterity Network | Paper | Project | Github |
| Cornell | Paper | Project | Github | |
| Jacquard | Paper | Project | Github | |
| VMRD | Paper | Project | Github | |
| grasp_multiObject | Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset. | Paper | Project | Github |
| OCID | Paper | Project | Github | |
| Grasp-Anything | Paper | Project | Github | |
| 6-DoF GraspNet | Paper | Project | Github | |
| EGAD | Paper | Project | Github | |
| ACRONYM | Paper | Project | Github | |
| MetaGraspNet | Paper | Project | Github | |
| YCB-Video | Paper | Project | Github | |
| GraspNet-1Billion | ||||
| PID-GraspNet |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| ur5-robotic-grasping | Learning to Grasp Objects in Highly Cluttered Environments using Deep Convolutional Neural Networks | Paper | Project | Github |
| pybullet-robot-envs | A Python package that collects robotic environments based on the PyBullet simulator, suitable to develop and test Reinforcement Learning algorithms on simulated grasping and manipulation applications. | Paper | Project | Github |
| GIGA | Paper | Project | Github | |
| Edge-Grasp-Network | grasp generation and process | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| grasping_sim | Leveraging Contact Forces for Learning to Grasp | Paper | Project | Github |
| grasping in gazebo | Grasping in GAZEBO robotics simulator. | Paper | Project | Github |
| airobot | Robot simulation repository for robotic grasping. | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| Generalized Anthropomorphic Functional Grasping with Minimal Demonstrations | Paper | Project | Github | |
| DexGraspNet | DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation | Paper | Project | Github |
| GenDexGrasp | GenDexGrasp: Generalizable Dexterous Grasping | Paper | Project | Github |
| HGC-Net | HGC-Net: Deep Anthropomorphic Hand Grasping in Clutter | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| DA2 | DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| SuctionNet-1Billion | Paper | Project | Github | |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| EfficientGrasp | EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands | Paper | Project | Github |
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
- Shuran Song @ Stanford | REAL @ Stanford
- Ken Goldberg @ Berkeley | AutoLab @ Berkeley
- Jeannette Bohg @ Stanford | IPRL Lab @ Stanford
- Wenzhen Yuan @ UIUC | Touch Lab @ UIUC
- Yuke Zhu @ UT Austin | RPL @ UT Austin
- Hao Su @ UCSD | Su Lab @ UCSD
- Xiaolong Wang @ UCSD
- IVA Lab @ Georgia Tech(Personnel)
- Yunzhi Lin @ Georgia Tech
- Fu-Jen Chu@ Georgia Tech
- grasp_multiObject
- grasp_multiObject_multiGrasp
- Keypoint-GraspNet
- Danica Kragic @ KTH
- Hamidreza Kasaei @ University of Groningen | IPL Lab @ University of Groningen
- Cewu Lu @ STJU | MVIG @ STJU
- Haoshu Fang @ STJU
- Chenxi Wang @ STJU
- Minghao Gou @ STJU
- Hongjie Fang @ STJU
- He Wang @ PKU | EPIC @ PKU
- Xuguang Lan @ XJTU
- IRIP Lab @ Beihang University
- Haoxiang Ma @ Beihang University
- Di Huang @ Beihang University
- Andy Zeng @ Google Deepmind
- RSS: Robotics: Science and Systems
- ICRA: IEEE International Conference on Robotics and Automation
- IROS: IEEE/RSJ International Conference on Intelligent Robots and Systems
- CoRL: Conference on Robot Learning
- CVPR: IEEE Conference on Computer Vision and Pattern Recognition
- ICCV: IEEE International Conference on Computer Vision
- ECCV: European Conference on Computer Vision
- NIPS: Conference on Neural Information Processing Systems
- RA-L: IEEE Robotics and Automation Letters
- Humanoids: IEEE-RAS International Conference on Humanoid Robots
- IJRR: The International Journal of Robotics Research
- ACM MM: ACM International Conference on Multimedia
- T-RO: IEEE Transactions on Robotics
- A Mathematical Introduction to Robotic Manipulation
- Fundamentals Of Robotic Grasping And Fixturing
- Grasping in Robotics
- Robotic Grasping and Manipulation
- Deep Learning Approaches to Grasp Synthesis: A Review, [Project], [Paper].
- Robotic Grasping from Classical to Modern: A Survey, [Project], [Paper].
- Vision‑based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review, [Project], [Paper].
- Data-Driven Grasp Synthesis - A Survey
| Name | Features | 📄Paper | 🌐Project | 📁Github |
|---|---|---|---|---|
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github | ||
| Paper | Project | Github |
- Multi-Views?
- Add depth to the planar grasping