On the closure properties of robotic grasping
International Journal of Robotics Research
Grasp metrics: optimality and complexity
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
On computing four-finger equilibrium and force-closure grasps of polyhedral objects
International Journal of Robotics Research
Mathematical Programming: Series A and B
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Hand Posture Subspaces for Dexterous Robotic Grasping
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
Gaussian Processes for Object Categorization
International Journal of Computer Vision
A new strategy combining empirical and analytical approaches for grasping unknown 3D objects
Robotics and Autonomous Systems
Robust robotic grasping force optimization with uncertainty
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Synthesizing grasp configurations with specified contact regions
International Journal of Robotics Research
A fast grasp synthesis method for online manipulation
Robotics and Autonomous Systems
An overview of 3D object grasp synthesis algorithms
Robotics and Autonomous Systems
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In everyday life, people use a large diversity of hand configurations while reaching out to grasp an object. They tend to vary their hands position/orientation around the object and their fingers placement on its surface according to the object properties such as its weight, shape, friction coefficient and the task they need to accomplish. Taking into account these properties, we propose a method for generating such a variety of good grasps that can be used for the accomplishment of many different tasks. Grasp synthesis is formulated as a single constrained optimization problem, generating grasps that are feasible for the hand's kinematics by minimizing the norm of the joint torque vector of the hand ensuring grasp stability. Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps. We show that the approach is adapted to different representations of the object surface and different hand kinematic models.