On the closure properties of robotic grasping
International Journal of Robotics Research
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Graspit!: a versatile simulator for robotic grasping
Graspit!: a versatile simulator for robotic grasping
Detection and Evaluation of Grasping Positions for Autonomous Agents
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
An efficient and robust algorithm for 3D mesh segmentation
Multimedia Tools and Applications
Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
IEEE Transactions on Visualization and Computer Graphics
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
On computing robust N-finger force-closure grasps of 3D objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An overview of 3D object grasp synthesis algorithms
Robotics and Autonomous Systems
A 3D shape segmentation approach for robot grasping by parts
Robotics and Autonomous Systems
Extracting data from human manipulation of objects towards improving autonomous robotic grasping
Robotics and Autonomous Systems
Graspable parts recognition in man-made 3d shapes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
ACM Transactions on Graphics (TOG)
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The paper presents a novel strategy that learns to associate a grasp to an unknown object/task. A hybrid approach combining empirical and analytical methods is proposed. The empirical step ensures task-compatibility by learning to identify the object graspable part in accordance with humans choice. The analytical step permits contact points generation guaranteeing the grasp stability. The robotic hand kinematics are also taken into account. The corresponding results are illustrated using GraspIt interface [1].