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

Showing 201246 of 246 papers

TitleStatusHype
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control0
Robotic Grasp Detection using Deep Convolutional Neural Networks0
Robotic Grasping of Fully-Occluded Objects using RF Perception0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning0
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration0
Robotics and Computer-Integrated Manufacturing0
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and InteractionsCode0
The CoSTAR Block Stacking Dataset: Learning with Workspace ConstraintsCode0
Towards Confidence-guided Shape Completion for Robotic ApplicationsCode0
Consensus-Driven Uncertainty for Robotic Grasping based on RGB PerceptionCode0
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement LearningCode0
Learning Object Placements For Relational Instructions by Hallucinating Scene RepresentationsCode0
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage TaskCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Jacquard: A Large Scale Dataset for Robotic Grasp DetectionCode0
3D Semantic Segmentation of Modular Furniture using rjMCMCCode0
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose EstimationCode0
PGA: Personalizing Grasping Agents with Single Human-Robot InteractionCode0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A ReviewCode0
Supertoroid fitting of objects with holes for robotic grasping and scene generationCode0
A Benchmarking Study of Vision-based Robotic Grasping AlgorithmsCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic GraspingCode0
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience ImaginationCode0
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation TasksCode0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
Adversarial samples for deep monocular 6D object pose estimationCode0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural NetworkCode0
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial SettingsCode0
DynGraspVS: Servoing Aided Grasping for Dynamic EnvironmentsCode0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for RoboticsCode0
State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic GraspingCode0
Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous RobotsCode0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Reward Engineering for Object Pick and Place TrainingCode0
Antipodal Robotic Grasping using Generative Residual Convolutional Neural NetworkCode0
Action Priors for Large Action Spaces in RoboticsCode0
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FlexLoG-CDmAP56.02Unverified
2GtG2.0mAP53.42Unverified
3Scale-Balanced-Grasp-CDmAP48.97Unverified
4graspness-CDmAP48.75Unverified
5HGGD-CDmAP47.54Unverified
6HGGDmAP44.24Unverified
7graspnet-baseline-CDmAP35.45Unverified
8graspnet-baselinemAP21.41Unverified
#ModelMetricClaimedVerifiedStatus
1grasp_det_seg_cnn (rgb only, IW split)5 fold cross validation98.2Unverified
2GR-ConvNet5 fold cross validation97.7Unverified
3ResNet50 multi-grasp predictor5 fold cross validation96Unverified
4Multi-Modal Grasp Predictor5 fold cross validation89.21Unverified
5AlexNet, MultiGrasp5 fold cross validation88Unverified
6GGCNN5 fold cross validation73Unverified
7Fast Search5 fold cross validation60.5Unverified
#ModelMetricClaimedVerifiedStatus
1Efficient-GraspingAccuracy (%)95.6Unverified
2GR-ConvNetAccuracy (%)94.6Unverified
3grasp_det_seg_cnn (rgb only)Accuracy (%)92.95Unverified