Papers with Code 아카이브
Robotic Grasping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 19
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
JENGA: Object selection and pose estimation for robotic grasping from a stack
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers
Grasp the Graph (GtG) 2.0: Ensemble of GNNs for High-Precision Grasp Pose Detection in Clutter
Category-Level and Open-Set Object Pose Estimation for Robotics
Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects
Free-form language-based robotic reasoning and grasping
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
A Benchmarking Study of Vision-based Robotic Grasping Algorithms
NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation
Attribute-Based Robotic Grasping with Data-Efficient Adaptation
Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs
Supertoroid fitting of objects with holes for robotic grasping and scene generation
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything
SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images
An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors
Towards Cross-device and Training-free Robotic Grasping in 3D Open World
VMGNet: A Low Computational Complexity Robotic Grasping Network Based on VMamba with Multi-Scale Feature Fusion
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent
ManiBox: Enhancing Spatial Grasping Generalization via Scalable Simulation Data Generation
Bayesian optimization for robust robotic grasping using a sensorized compliant hand
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models
Target-Oriented Object Grasping via Multimodal Human Guidance
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.