Perceptual organization and the representation of natural form
Artificial Intelligence
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
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
Using Humanoid Robots to Study Human Behavior
IEEE Intelligent Systems
Fixturing faceted parts with seven modular struts
ISATP '95 Proceedings of the 1995 IEEE International Symposium on Assembly and Task Planning
SMI '04 Proceedings of the Shape Modeling International 2004
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
Superquadrics and Angle-Preserving Transformations
IEEE Computer Graphics and Applications
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
Functional object class detection based on learned affordance cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
An overview of 3D object grasp synthesis algorithms
Robotics and Autonomous Systems
Extracting data from human manipulation of objects towards improving autonomous robotic grasping
Robotics and Autonomous Systems
A biomimetic reach and grasp approach for mechanical hands
Robotics and Autonomous Systems
On the generation of a variety of grasps
Robotics and Autonomous Systems
Stable grasping under pose uncertainty using tactile feedback
Autonomous Robots
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This paper proposes a novel strategy for grasping 3D unknown objects in accordance with their corresponding task. We define the handle or the natural grasping component of an object as the part chosen by humans to pick up this object. When humans reach out to grasp an object, it is generally in the aim of accomplishing a task. Thus, the chosen grasp is quite related to the object task. Our approach learns to identify object handles by imitating humans. In this paper, a new sufficient condition for computing force-closure grasps on the obtained handle is also proposed. Several experiments were conducted to test the ability of the algorithm to generalize to new objects. They also show the adaptability of our strategy to the hand kinematics.