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Combinatorial optimization for hierarchical contact-level grasping
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-4132-1217
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-1114-6040
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2014 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2014, 381-388 p.Conference paper, Published paper (Refereed)
Abstract [en]

We address the problem of generating force-closed point contact grasps on complex surfaces and model it as a combinatorial optimization problem. Using a multilevel refinement metaheuristic, we maximize the quality of a grasp subject to a reachability constraint by recursively forming a hierarchy of increasingly coarser optimization problems. A grasp is initialized at the top of the hierarchy and then locally refined until convergence at each level. Our approach efficiently addresses the high dimensional problem of synthesizing stable point contact grasps while resulting in stable grasps from arbitrary initial configurations. Compared to a sampling-based approach, our method yields grasps with higher grasp quality. Empirical results are presented for a set of different objects. We investigate the number of levels in the hierarchy, the computational complexity, and the performance relative to a random sampling baseline approach.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 381-388 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-176144DOI: 10.1109/ICRA.2014.6906885Scopus ID: 2-s2.0-84929171240OAI: oai:DiVA.org:kth-176144DiVA: diva2:875056
Conference
2014 IEEE International Conference on Robotics and Automation, ICRA 2014, 31 May 2014 through 7 June 2014
Note

QC 20151130

Available from: 2015-11-30 Created: 2015-11-02 Last updated: 2016-05-16Bibliographically approved
In thesis
1. Dexterous Grasping: Representation and Optimization
Open this publication in new window or tab >>Dexterous Grasping: Representation and Optimization
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many robot object interactions require that an object is firmly held, and that the grasp remains stable during the whole manipulation process. Based on grasp wrench space, this thesis address the problems of measuring the grasp sensitivity against friction changes, planning contacts and hand configurations on mesh and point cloud representations of arbitrary objects, planning adaptable grasps and finger gaiting for keeping a grasp stable under various external disturbances, as well as learning of grasping manifolds for more accurate reachability and inverse kinematics computation for multifingered grasping. 

Firstly, we propose a new concept called friction sensitivity, which measures how susceptible a specific grasp is to changes in the underlying frictionc oefficients. We develop algorithms for the synthesis of stable grasps with low friction sensitivity and for the synthesis of stable grasps in the case of small friction coefficients.  

Secondly, for fast planning of contacts and hand configurations for dexterous grasping, as well as keeping the stability of a grasp during execution, we present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage and external disturbances. For this purpose, we introduce the Hierarchical Fingertip Space (HFTS) as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. 

Lastly, to improve the efficiency and accuracy of dexterous grasping and in-hand manipulation, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. During execution the system plans and executes fingertip grasps using Canny’s grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 167 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 14
Keyword
Dexterous Grasping, Hierarchical Fingertip Space, Grasp Planning, Grasp Adaptation
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-186158 (URN)978-91-7595-993-1 (ISBN)
Public defence
2016-06-03, D2, Lindstedtsvägen 5, Stockholm, 13:25 (English)
Opponent
Projects
Flexbot
Funder
EU, European Research Council, 6138
Note

QC 20160516

Available from: 2016-05-16 Created: 2016-05-03 Last updated: 2016-05-18Bibliographically approved

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Pokorny, Florian T.Kragic, Danica

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