NCCR Robotics is a consortium of robotics laboratories across Switzerland, working on robots for improving the quality of life and to strengthen robotics in Switzerland and worldwide. Newsletter
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Published in: IEEE Robotics and Automation Letters (RA-L) Gripper adaptability to handle objects of different shape and size brings high flexibility to manipulation. Gripping flat, round, or narrow objects poses challenges to even the most sophisticated robotic grippers. Among various gripper technologies, the vacuum suction grippers provide design simplicity, yet versatility at low cost, …
This paper addresses the problem of optimal grasping of an object with a multi-fingered robotic hand for accomplishing a given task. The task is first demonstrated by a human operator and its force/torque requirements are captured through the usage of a sensorized tool. The grasp quality is computed through a task compatibility criterion. Grasp synthesis is then formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by maximizing the corresponding task-oriented quality criterion and ensuring grasp stability. The method was validated on a human hand model and is shown to be easily adapted to different hand kinematic models.
In everyday life, people use a large diversity of hands 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.