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 151–200 of 246 papers

TitleStatusHype
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping—0
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Adversarial samples for deep monocular 6D object pose estimationCode0
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
MPF6D: Masked Pyramid Fusion 6D Pose Estimation—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
When Neural Networks Using Different Sensors Create Similar Features—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
Robust Extrinsic Symmetry Estimation in 3D Point Clouds—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions—0
Domestic waste detection and grasping points for robotic picking up—0
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
Attribute-Based Robotic Grasping with One-Grasp Adaptation—0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Collision-Aware Target-Driven Object Grasping in Constrained Environments—0
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping—0
DDGC: Generative Deep Dexterous Grasping in Clutter—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
Cooking Object's State Identification Without Using Pretrained Model—0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection—0
Action Priors for Large Action Spaces in RoboticsCode0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial SettingsCode0
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss—0
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN—0
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction—0
PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real—0
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience ImaginationCode0
Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting—0
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image—0
Learning Object Placements For Relational Instructions by Hallucinating Scene RepresentationsCode0
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing—0
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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