Real-World Multiobject, Multigrasp Detection
Explore this paper's citation graph
- Type
- article
- Published
- 2018-07-04
- Cited by
- 381
- References
- 32
- OpenAlex
- https://openalex.org/W2824754393
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:50778716
Keywords
Artificial intelligence, GRASP, Computer science, Generalization, Object (grammar)
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Robotic grasping and contact: a review
- Real-time grasp detection using convolutional neural networks
- Robot Grasp Synthesis Algorithms: A Survey
- Deep learning for detecting robotic grasps
- An overview of 3D object grasp synthesis algorithms
- Grasping novel objects with depth segmentation
- Learning to grasp objects with multiple contact points
- Data-Driven Grasp Synthesis—A Survey
- Robotic Grasping of Novel Objects using Vision
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning
- Efficient grasping from RGBD images: Learning using a new rectangle representation
- Selection of robot pre-grasps using box-based shape approximation
- Learning to grasp using visual information
- ImageNet classification with deep convolutional neural networks
- Multimodal Deep Learning
- Deep Residual Learning for Image Recognition
- Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
- Deep learning a grasp function for grasping under gripper pose uncertainty
Cited by
- Review of Deep Learning Methods in Robotic Grasp Detection
- Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images
- Densely Supervised Grasp Detector (DSGD)
- Rotation Ensemble Module for Detecting Rotation-Invariant Features
- A Panoramic Survey on Grasping Research Trends and Topics
- Learning Affordance Segmentation for Real-World Robotic Manipulation via Synthetic Images
- Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
- GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier
- Toward Affordance Detection and Ranking on Novel Objects for Real-World Robotic Manipulation
- A Real-Time Robotic Grasping Approach With Oriented Anchor Box
- Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
- Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module
- A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection
- Detecting Robotic Affordances on Novel Objects with Regional Attention and Attributes
- Object grasping planning for the situation when soft and rigid objects are mixed together
- S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes
- GraspCNN: Real-Time Grasp Detection Using a New Oriented Diameter Circle Representation
- Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning
- GraspNet: A Large-Scale Clustered and Densely Annotated Datase for Object Grasping
- Visual-Guided Robot Arm Using Multi-Task Faster R-CNN
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