Robotic Grasping
80 papers with code • 4 benchmarks • 16 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
CONG
NBMOD
Grasp-Anything
rc_49
Latest papers
NBMOD: Find It and Grasp It in Noisy Background
kmittle/grasp-detection-nbmod
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In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset.
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network
In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability.
DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping
AnasIbrahim/image_agnostic_segmentation
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In this work, we are interested in open sets where the number of classes is unknown, varying, and without pre-knowledge about the objects' types.
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping
Object segmentation for robotic grasping under dynamic conditions often faces challenges such as occlusion, low light conditions, motion blur and object size variance.
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement
zhirui-gao/deep-template-matching
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To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement.
Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging.
Depth-based 6DoF Object Pose Estimation using Swin Transformer
To tackle this challenge, we propose a novel framework called SwinDePose, that uses only geometric information from depth images to achieve accurate 6D pose estimation.
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.
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
youngsean/unseenobjectswithmeanshift
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By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way.
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes
mahaoxiang822/scale-balanced-grasp
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Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the samples whose scales are in low frequency by apriori weights while OBS captures more points on small-scale objects with the help of an auxiliary segmentation network.
