Contact-free grasp stability prediction using in-hand multi-zone time-of-flight sensors achieves 86% accuracy on unseen objects.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2representative citing papers
GET-2D-1.0 and GET-3D-1.0 grasp planners for the GET asymmetrical gripper achieve over 40% better lift success, shake survival, and force resistance than a bounding-box baseline in physical robot tests.
citing papers explorer
-
Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors
Contact-free grasp stability prediction using in-hand multi-zone time-of-flight sensors achieves 86% accuracy on unseen objects.
-
2D and 3D Grasp Planners for the GET Asymmetrical Gripper
GET-2D-1.0 and GET-3D-1.0 grasp planners for the GET asymmetrical gripper achieve over 40% better lift success, shake survival, and force resistance than a bounding-box baseline in physical robot tests.