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Grasp Generation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Unified Transformer-Based Framework with Pretraining For Whole Body Grasping Motion Generation
Exploiting Radiance Fields for Grasp Generation on Novel Synthetic Views
Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation
EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
Task-Oriented 6-DoF Grasp Pose Detection in Clutters
Controllable Hand Grasp Generation for HOI and Efficient Evaluation Methods
Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation
RegionGrasp: A Novel Task for Contact Region Controllable Hand Grasp Generation
Multi-Modal Diffusion for Hand-Object Grasp Generation
Learning Precise Affordances from Egocentric Videos for Robotic Manipulation
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation
NL2Contact: Natural Language Guided 3D Hand-Object Contact Modeling with Diffusion Model
HUP-3D: A 3D multi-view synthetic dataset for assisted-egocentric hand-ultrasound pose estimation
Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting
Single-View Scene Point Cloud Human Grasp Generation
Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping
Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation
SemGrasp: Semantic Grasp Generation via Language Aligned Discretization
Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
DexDiffuser: Generating Dexterous Grasps with Diffusion Models
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation
UGG: Unified Generative Grasping
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
ContactGen: Generative Contact Modeling for Grasp Generation
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes
Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering
KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.