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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 246 papers

TitleStatusHype
Prompting Depth Anything for 4K Resolution Accurate Metric Depth EstimationCode5
Evaluating Real-World Robot Manipulation Policies in SimulationCode5
Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU SimulationCode2
Free-form language-based robotic reasoning and graspingCode2
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
An Economic Framework for 6-DoF Grasp DetectionCode2
Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic GraspingCode2
Generalizing 6-DoF Grasp Detection via Domain Prior KnowledgeCode2
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic GraspingCode2
Grasp-Anything: Large-scale Grasp Dataset from Foundation ModelsCode2
You Only Demonstrate Once: Category-Level Manipulation from Single Visual DemonstrationCode2
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied AgentsCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable ScenesCode1
NBMOD: Find It and Grasp It in Noisy BackgroundCode1
DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic GraspingCode1
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence RefinementCode1
Amodal Intra-class Instance Segmentation: Synthetic Datasets and BenchmarkCode1
Depth-based 6DoF Object Pose Estimation using Swin TransformerCode1
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking ApplicationsCode1
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot InteractionCode1
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered ScenesCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-ResolutionsCode1
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic GraspingCode1
Action Conditioned Tactile Prediction: case study on slip predictionCode1
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and BenchmarkCode1
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp DetectionCode1
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse SynthesisCode1
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation TasksCode1
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking TasksCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
CLIPort: What and Where Pathways for Robotic ManipulationCode1
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement AlgorithmsCode1
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBCode1
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in ClutterCode1
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered ScenesCode1
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
Object SLAM-Based Active Mapping and Robotic GraspingCode1
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented GraspingCode1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
Learning Dexterous Grasping with Object-Centric Visual AffordancesCode1
Grasping Field: Learning Implicit Representations for Human GraspsCode1
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map RepresentationCode1
GraspNet-1Billion: A Large-Scale Benchmark for General Object GraspingCode1
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream DatasetCode1
Show:102550
← PrevPage 1 of 5Next →
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