Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2.8GHz processor.

Optimal grasp synthesis has traditionally been solved in two steps: determining optimal grasping points according to a specific quality criterion and then determining how to shape the hand to produce these grasping points. Generating optimal grasps depends on the position of contact points as much as the configuration of the robot hand and it would hence be desirable to solve this in a single step. This paper takes advantage of new development in non-linear optimization and formulates the problem of grasp synthesis as a single constrained optimization problem, generating grasps that are at the same time feasible for the hand's kinematics and optimal according to a force related quality measure. The approach is validated on the 9 degrees of freedom hand of the iCub humanoid robot.

The paper deals with computing frictional force-closure grasps of 3D objects problem. The key idea of the presented work is the demonstration that wrenches associated to any three non-aligned contact points of 3D objects form a basis of their corresponding wrench space. This result permits the formulation of a new sufficient force-closure test. Our approach works with general objects, modelled with a set of points, and with any number n of contacts (n ges 4). A quality criterion is also introduced. A corresponding algorithm for computing robust force-closure grasps has been developed. Its efficiency is confirmed by comparing it to the classical convex-hull method [26].

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.

The stability of a grasp is characterized by force-closure property, under which arbitrary forces and torques exerted on the grasped object can be balanced by the contact forces applied by the fingers. Existing force-closure tests require considerable computation time. Thus, heuristic approaches are a way to improve performance. In this work, we propose a sufficient but not necessary method to compute force-closure grasps of 3D objects. Our approach works with general objects and with any number n of contacts (nges4).

A grasp is the beginning of any manipulation task. Therefore, an autonomous robot should be able to grasp objects it sees for the first time. It must hold objects appropriately in order to successfully perform the task. This paper considers the problem of grasping unknown objects in the same manner as humans. Based on the idea that the human brain represents objects as volumetric primitives in order to recognize them, the presented algorithm predicts grasp as a function of the object's parts assembly. Beginning with a complete 3D model of the object, a segmentation step decomposes it into single parts. Each single part is fitted with a simple geometric model. A learning step is finally needed in order to find the object component that humans choose to grasp it.

In this paper, we propose a new method for the motion planning problem of rigid object dexterous manipulation with a robotic multi-fingered hand, under quasi-static movement assumption. This method computes both object and finger trajectories as well as the finger relocation sequence. Its specificity is to use a special structuring of the research space that allows to search for paths directly in the particular subspace GSnwhich is the subspace of all the grasps that can be achieved with n grasping fingers. The solving of the dexterous manipulation planning problem is based upon the exploration of this subspace. The proposed approach captures the connectivity of GSnin a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method.