Robotic Grasping Comparison Hub
4 papers - avg viability 7.3
Top Papers
- AdaClearGrasp: Learning Adaptive Clearing for Zero-Shot Robust Dexterous Grasping in Densely Cluttered Environments(8.0)
AdaClearGrasp enables robots to adaptively decide between clearing obstacles or grasping targets in cluttered environments for improved manipulation.
- GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions(7.0)
GraspALL enhances robotic garment grasping accuracy in low-light conditions through adaptive feature fusion of RGB and non-RGB modalities.
- Dexterous grasp data augmentation based on grasp synthesis with fingertip workspace cloud and contact-aware sampling(7.0)
A teleoperation-based framework for efficient data augmentation in robotic grasping using fingertip contact-aware sampling.
- MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations(7.0)
MG-Grasp is a depth-free 6-DoF grasping framework that enhances robotic manipulation using sparse RGB observations.