Efficient determination of four-point form-closure optimal constraints of polygonal objects

This paper proposes a new and more efficient solution to the problem of determining optimal form-closure constraints of polygonal objects using four contacts. New grasp parameters are determined based only on the directions of the applied forces, which are then used to determine the optimal grasp. Given a set of contact edges, using an analytical procedure a solution that is either the optimal one or is very close to it is obtained (only in this second case an iterative procedure is needed to find a root of a nonlinear equation). This procedure is used for an efficient determination of the optimal grasp on the whole object. The algorithms have been implemented and numerical examples are shown. Note to Practitioners—This paper presents an algorithm that improves previous approaches in terms of efficiency in the determination of the optimal object constraint maximizing the minimum wrench that the object can support in any direction. The problem can always be solved using numerical optimization techniques but when time is relevant an efficient algorithm becomes of interest. Practical applications include optimal determination of fixtures and object grasps.Peer ReviewedPostprint (published version

Searching force-closure optimal grasps of articulated 2D objects with n links

This paper proposes a method that finds a locally optimal grasp of an articulated 2D object with n links considering frictionless contacts. The surface of each link of the object is represented by a finite set of points, thus it may have any shape. The proposed approach finds, first, an initial force-closure grasp and from it starts an iterative search of a local optimum grasp. The quality measure considered in this work is the largest perturbation wrench that a grasp can resist with independence of the direction of the perturbation. The approach has been implemented and some illustrative examples are included in the article.Postprint (published version

Grasping bulky objects with two anthropomorphic hands

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper inclu des some application examples of the proposed approachAccepted versio

Determination of seven frictionless fixturing points searching the object surface with a homogeneous deterministic distribution

The paper deals whit the problem of finding a form-closure fixturing of objects modeled whit triangular meshes and considering as quality measure the maximum wrench that the object can resist in any direction. Although a triangular mesh is a polyhedral representation of the object, the number of faces is too large to allow a practical application of existing approaches for polyhedral objects, and therefore some search procedure have to be applied. In the proposed approach the search of contact points is done looking for points directly on the object boundary instead of on the wrench space. In this way, all the object surface is homogeneously considered, while the quality is evaluated in the wrench space. The procedure iteratively looks, using heuristic criteria, for sets of points that improve the quality. The procedure was implemented and some application examples are included in the paper to illustrate the performanc

Computation of independent contact regions for grasping 3-D objects

Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance.Peer Reviewe

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201

Determining force-closure grasps reachable by a given hand

The paper presents an approach to find contact points on an object surface that are reachable by a given hand and such that the resulting grasp satisfies the force-closure condition. This is a very common problem that still requires a practical solution. The proposed method is based on the computation of a set of independent contact regions on the object boundary such that a finger contact on each region produces a force-closure grasp, and then this set of regions is iteratively recomputed while looking for a set of contact points that are reachable by a given hand. The search is done guided by a cost function that indicates the proximity of the hand fingertips to a candidate set of grasping contact points. The approach has been implemented for the Schunk Anthropomorphic Hand and planar objects,and application examples are included to illustrate its performance.Postprint (published version

Searching a valid hand configuration to perform a given grasp

Peer ReviewedPostprint (published version

Frictionless grasp with 7 fingers on discretized 3D objects

This paper presents an algorithm to plain locally frictionless grasp on 3D objects. The objects can be of any arbitrary shape, since the surface is discretized in a cloud of points. The planning algorithm finds an initial force-closure grasp that is iteratively improved through an oriented search procedure. The grasp quality is measured with the “largest ball” criterion, and a force-closure test based on geometric considerations is used. The efficiency of the algorithm is illustrated through numerical example