On the synthesis of feasible and prehensile robotic grasps

Rosales, Carlos; Suárez, Raúl; Gabiccini, Marco; Bicchi, Antonio

doi:10.1109/icra.2012.6225238

Cited by 50 publications

(33 citation statements)

References 32 publications

(24 reference statements)

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“…Finally, the robot verifies that the grasp is kinematically feasible, i.e., that the hand can be moved to a configuration that realises those contacts (Rosales et al, 2011). Force analysis is applicable to multi-fingered hands, and its ability to generate complex grasps has been shown in the literature (Boutselis et al, 2014;Gori et al, 2014;Grupen, 1991;Hang et al, 2014;Rosales et al, 2012;Saut & Sidobre, 2012;Xu et al, 2007).…”

Section: Ijrr -(-)mentioning

confidence: 99%

One-shot learning and generation of dexterous grasps for novel objects

Kopicki

Detry

Adjigble

et al. 2015

The International Journal of Robotics Research

115

163

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This paper presents a method for one-shot learning of dexterous grasps, and grasp generation for novel objects. A model of each grasp type is learned from a single kinesthetic demonstration, and several types are taught. These models are used to select and generate grasps for unfamiliar objects. Both the learning and generation stages use an incomplete point cloud from a depth camera -no prior model of object shape is used. The learned model is a product of experts, in which experts are of two types. The first is a contact model and is a density over the pose of a single hand link relative to the local object surface. The second is the hand configuration model and is a density over the whole hand configuration. Grasp generation for an unfamiliar object optimises the product of these two model types, generating thousands of grasp candidates in under 30 seconds. The method is robust to incomplete data at both training and testing stages. When several grasp types are considered the method selects the highest likelihood grasp across all the types. In an experiment, the training set consisted of five different grasps, and the test set of forty-five previously unseen objects. The success rate of the first choice grasp is 84.4% or 77.7% if seven views or a single view of the test object are taken, respectively. Keywords learning, dexterous grasping * Authors Kopicki, Detry and Wyatt are identified as the primary authors of this work. Kopicki is identified as the first author. † Corresponding author; e-mail: jlw@cs.bham.ac.uk 2 IJRR -(-) Fig. 1. Leftmost image: Objects used, the four objects on the left were used solely for training, the remaining forty three objects on the right were solely used as novel test objects. Rightmost image: The Boris manipulation platform on which the experiments reported were carried out.

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Section: Ijrr -(-)mentioning

confidence: 99%

One-shot learning and generation of dexterous grasps for novel objects

Kopicki

Detry

Adjigble

et al. 2015

The International Journal of Robotics Research

115

163

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“…This requires to take into account dynamics, control, and path planning issues. To this end, the pipeline is being extended using impedance control techniques based on a kinestatic analysis of the grasp [44], and with a planner to compute an approach path to the object to be grasped [37]. Also, an analysis of the uncertainty would be necessary to achieve a successful execution of the grasp.…”

Section: Discussionmentioning

confidence: 99%

Grasp Optimization Under Specific Contact Constraints

2013

Self Cite

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Abstract-This paper presents a procedure to synthesize highquality grasps for objects that need to be held and manipulated in a specific way, characterized by a pre-specified set of contact constraints to be satisfied. Due to the multi-modal nature of typical grasp quality measures, approaches that resort to local optimization methods are likely to get trapped into local extrema on such problem. An additional difficulty of the problem is that the set of feasible grasps is a highly-dimensional manifold, implicitly defined by a system of non-linear equations. The proposed procedure finds a way around these issues by focusing the exploration on a relevant subset of grasps of lower dimension, and tracing this subset exhaustively using a higher-dimensional continuation technique. A detailed atlas of the subset is obtained as a result, on which the highest-quality grasp according to any desired criterion, or a combination of criteria, can be readily identified. Examples are included that illustrate the application of the method to a three-fingered planar hand and to the Schunk anthropomorphic hand grasping several objects, using several quality indices.

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“…However they neither account for stability nor feasibility of the grasp. Yet other methods start from computing a feasible hand configuration for a set of given stable points [10] [11]. There are also "hybrid" approaches [12] that try to provide contact points that are stable, feasible or both.…”

Section: Introductionmentioning

confidence: 99%

Three-finger precision grasp on incomplete 3D point clouds

Pattacini

Tikhanoff

Metta

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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We present a novel method for three-finger precision grasp and its implementation in a complete grasping toolchain. We start from binocular vision to recover the partial 3D structure of unknown objects. We then process the incomplete 3D point clouds searching for good triplets according to a function that accounts for both the feasibility and the stability of the solution. In particular, while stability is determined using the classical force-closure approach, feasibility is evaluated according to a new measure that includes information about the possible configuration shapes of the hand as well as the hand's inverse kinematics. We finally extensively assess the proposed method using the stereo vision and the kinematics of the iCub robot.

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On the synthesis of feasible and prehensile robotic grasps

Cited by 50 publications

References 32 publications

One-shot learning and generation of dexterous grasps for novel objects

One-shot learning and generation of dexterous grasps for novel objects

Grasp Optimization Under Specific Contact Constraints

Three-finger precision grasp on incomplete 3D point clouds

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