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Hierarchical Fingertip Space for Multi-fingered Precision Grasping
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.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. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2014 (English)In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE , 2014, 1641-1648 p.Conference paper, Published paper (Refereed)
Abstract [en]

Dexterous in-hand manipulation of objects benefits from the ability of a robot system to generate precision grasps. In this paper, we propose a concept of Fingertip Space and its use for precision grasp synthesis. Fingertip Space is a representation that takes into account both the local geometry of object surface as well as the fingertip geometry. As such, it is directly applicable to the object point cloud data and it establishes a basis for the grasp search space. We propose a model for a hierarchical encoding of the Fingertip Space that enables multilevel refinement for efficient grasp synthesis. The proposed method works at the grasp contact level while not neglecting object shape nor hand kinematics. Experimental evaluation is performed for the Barrett hand considering also noisy and incomplete point cloud data.

Place, publisher, year, edition, pages
IEEE , 2014. 1641-1648 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keyword [en]
Contact levels, Experimental evaluation, Grasp synthesis, Hand kinematics, Hand manipulation, Hierarchical encoding, Multilevel refinement, Point cloud data
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-163508DOI: 10.1109/IROS.2014.6942775ISI: 000349834601110Scopus ID: 2-s2.0-84911484070ISBN: 978-1-4799-6934-0 (print)OAI: oai:DiVA.org:kth-163508DiVA: diva2:800687
Conference
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Palmer House Hilton Hotel Chicago, United States, 14 September 2014 through 18 September 2014
Note

QC 20150407

Available from: 2015-04-07 Created: 2015-04-07 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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