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robots-10

Robotic Grasping

3260 papers • 126 benchmarks • 313 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.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in robotic-grasping-21

Trend

Dataset

Best Model

Actions

Cornell Grasp Dataset

Cornell Grasp Dataset

Jacquard dataset

Jacquard dataset

GraspNet-1Billion

Libraries

i

Use these libraries to find robotic-grasping-21 models and implementations

Datasets

Cornell

Dex-Net 2.0

EGAD

RoboNet

GraspNet-1Billion

GraspNet-1Billion

A Billion Ways to Grasp

A Billion Ways to Grasp

Subtasks

Grasp Contact Prediction

Most implemented papers

Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

Sulabh Kumra, Shirin Joshi, F. Sahin•Tue Sep 10 2019

A novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (∼20ms) is proposed.

356

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

GraspNet-1Billion

NBMOD

NBMOD

Grasp MultiObject

Grasp MultiObject

rc_49

NBMOD

Grasp-Anything

0

The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints

Gregory Hager, Andrew Hundt, Varun Jain, Chia-Hung Lin, Chris Paxton•Fri Oct 26 2018

It is shown 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.

12 0

Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching

Andy Zeng, Shuran Song, Alberto Rodriguez, T. Funkhouser, Kuan-Ting Yu, Nima Fazeli, Ferran Alet, E. Donlon, F. Hogan, Maria Bauzá, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, Nikhil Chavan Dafle, Rachel Holladay, I. Morona, P. Nair, Druck Green, Ian H. Taylor, Weber Liu•Mon Oct 02 2017

A robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments and that handles a wide range of object categories without needing any task-specific training data for novel objects is presented.

667 0

Grasping Field: Learning Implicit Representations for Human Grasps

Michael J. Black, Siyu Tang, Yan Zhang, Jinlong Yang, Korrawe Karunratanakul, Krikamol Muandet•Sun Aug 09 2020

The generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods.

242 0

Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications

Weiyu Feng, Seth Z. Zhao, Chuanyu Pan, Adam Chang, Yichen Chen, Zekun Wang, A. Yang•Sat Feb 11 2023

This work creates a novel RGB-D dataset, called Digital Twin Tracking Dataset (DTTD), to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy, and demonstrates that DTTD can help researchers develop future object tracking methods and analyze new challenges.

11 0

Real-World Multiobject, Multigrasp Detection

Fu-Jen Chu, Ruinian Xu, P. Vela•Tue Jul 03 2018

A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classified with null hypothesis competition instead of regression, the deep neural network with red, green, blue and depth (RGB-D) image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on imagewise and objectwise splits, respectively. Evaluation on a multiobject dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 89.0% grasping success rates on a test set of household objects. The real-time process takes less than 0.25 s from image to plan.

356 0

Real-time grasp detection using convolutional neural networks

Joseph Redmon, A. Angelova•Mon Dec 08 2014

An accurate, real-time approach to robotic grasp detection based on convolutional neural networks that outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU.

846 0

PyRobot: An Open-source Robotics Framework for Research and Benchmarking

A. Gupta, Saurabh Gupta, Lerrel Pinto, Dhiraj Gandhi, Adithyavairavan Murali, Tao Chen, Kalyan Vasudev Alwala•Tue Jun 18 2019

PyRobot is a light-weight, high-level interface on top of ROS that provides a consistent set of hardware independent mid-level APIs to control different robots, and will reduce the entry barrier into robotics, and democratize robotics.

113 0

Learning Object Placements For Relational Instructions by Hallucinating Scene Representations

Wolfram Burgard, Oier Mees, Alp Emek, Johan Vertens•Wed Jan 22 2020

This work presents a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image, and demonstrates the effectiveness of the method in reasoning about the best way to place objects to reproduce a spatial relation.

30 0

Composing Pick-and-Place Tasks By Grounding Language

Wolfram Burgard, Oier Mees•Mon Feb 15 2021

This work presents a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues and demonstrates the effectiveness of the method in understanding pick-and-place language instructions and sequentially composing them to solve tabletop manipulation tasks.

37 0

Adding a benchmark result helps the community track progress.

Robotic Grasping | State-of-the-Art