SOTAVerified

Robotic Grasping

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.

Papers

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Showing 26–50 of 246 papers

TitleStatusHype
Grasping Field: Learning Implicit Representations for Human GraspsCode1
Real-world multiobject, multigrasp detectionCode1
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in ClutterCode1
Action Conditioned Tactile Prediction: case study on slip predictionCode1
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied AgentsCode1
DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic GraspingCode1
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and BenchmarkCode1
Learning Dexterous Grasping with Object-Centric Visual AffordancesCode1
CLIPort: What and Where Pathways for Robotic ManipulationCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking TasksCode1
Amodal Intra-class Instance Segmentation: Synthetic Datasets and BenchmarkCode1
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse SynthesisCode1
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered ScenesCode1
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream DatasetCode1
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation TasksCode1
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence RefinementCode1
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking ApplicationsCode1
Depth-based 6DoF Object Pose Estimation using Swin TransformerCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
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All datasetsGraspNet-1BillionCornell Grasp DatasetJacquard dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FlexLoG-CDmAP56.02—Unverified
2GtG2.0mAP53.42—Unverified
3Scale-Balanced-Grasp-CDmAP48.97—Unverified
4graspness-CDmAP48.75—Unverified
5HGGD-CDmAP47.54—Unverified
6HGGDmAP44.24—Unverified
7graspnet-baseline-CDmAP35.45—Unverified
8graspnet-baselinemAP21.41—Unverified
#ModelMetricClaimedVerifiedStatus
1grasp_det_seg_cnn (rgb only, IW split)5 fold cross validation98.2—Unverified
2GR-ConvNet5 fold cross validation97.7—Unverified
3ResNet50 multi-grasp predictor5 fold cross validation96—Unverified
4Multi-Modal Grasp Predictor5 fold cross validation89.21—Unverified
5AlexNet, MultiGrasp5 fold cross validation88—Unverified
6GGCNN5 fold cross validation73—Unverified
7Fast Search5 fold cross validation60.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Efficient-GraspingAccuracy (%)95.6—Unverified
2GR-ConvNetAccuracy (%)94.6—Unverified
3grasp_det_seg_cnn (rgb only)Accuracy (%)92.95—Unverified