Robotic Grasping
87 papers with code • 4 benchmarks • 20 datasets
This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.
Benchmarks
These leaderboards are used to track progress in Robotic Grasping
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Libraries
Use these libraries to find Robotic Grasping models and implementationsDatasets
Cornell
Dex-Net 2.0
EGAD
GraspNet-1Billion
robosuite Benchmark
Grasp-Anything
CONG
Calandra Dataset
Most implemented papers
Rethinking the Inception Architecture for Computer Vision
tensorflow/models
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CVPR 2016
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from n-channel image of the scene.
Grasping Field: Learning Implicit Representations for Human Grasps
korrawe/grasping_field_demo
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Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
Real-Time Grasp Detection Using Convolutional Neural Networks
DucTranVan/grasp-detection-pytorch
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We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
augcog/dttdv1
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Digital twin is a problem of augmenting real objects with their digital counterparts.
Real-world multiobject, multigrasp detection
A deep learning architecture is proposed to predict graspable locations for robotic manipulation.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user.
