Classification based Grasp Detection using Spatial Transformer Network
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Summary
This work proposes a novel classification based robotic grasp detection method with multiple-stage spatial transformer networks (STN) that was able to achieve state-of-the-art performance in accuracy with real- time computation.
- Type
- preprint
- Published
- 2018-03-04
- Cited by
- 38
- References
- 23
- Access
- Open access
- OpenAlex
- https://openalex.org/W2791026634
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3680801
Keywords
GRASP, Artificial intelligence, Computer science, Observability, Computation
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- You Only Look Once: Unified, Real-Time Object Detection
- Real-time grasp detection using convolutional neural networks
- Deep learning for detecting robotic grasps
- An overview of 3D object grasp synthesis algorithms
- Data-Driven Grasp Synthesis—A Survey
- Robotic Grasping of Novel Objects using Vision
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Automatic grasp planning using shape primitives
- Efficient grasping from RGBD images: Learning using a new rectangle representation
- ImageNet classification with deep convolutional neural networks
- Learning grasping points with shape context
- Deep Residual Learning for Image Recognition
- Robot grasp detection using multimodal deep convolutional neural networks
- Robotic grasp detection using deep convolutional neural networks
- YOLO9000: Better, Faster, Stronger
- RGB-D Object Recognition and Grasp Detection Using Hierarchical Cascaded Forests
- Spatial Transformer Networks
- End-to-End Training of Deep Visuomotor Policies
- Deep Learning
Cited by
- Review of Deep Learning Methods in Robotic Grasp Detection
- Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
- Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot
- GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier
- Multi-Object Grasping Detection With Hierarchical Feature Fusion
- Vision-based Robotic Grasp Detection From Object Localization, Object Pose Estimation To Grasp Estimation: A Review.
- Visuo-Haptic Grasping of Unknown Objects through Exploration and Learning on Humanoid Robots
- Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
- Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensors
- Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
- Real-Time Deep Learning Approach to Visual Servo Control and Grasp Detection for Autonomous Robotic Manipulation
- Dog and Cat Classification with Deep Residual Network
- Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging
- Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection
- 6-DoF Contrastive Grasp Proposal Network
- Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization
- Data-efficient learning of object-centric grasp preferences
- FFHNet: Generating Multi-Fingered Robotic Grasps for Unknown Objects in Real-time
- Research Status of Robot Grab Detection Based on Vision
- MLAN: Multi-Level Attention Network
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