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Dexterous Grasping: Representation and Optimization
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. CVAP/CAS/CSC, KTH Royal Institute of Technology. (CVAP)ORCID iD: 0000-0003-4132-1217
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 [en]
Dexterous Grasping, Hierarchical Fingertip Space, Grasp Planning, Grasp Adaptation
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186158ISBN: 978-91-7595-993-1OAI: oai:DiVA.org:kth-186158DiVA: diva2:925809
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
List of papers
1. Friction Coefficients and Grasp Synthesis
Open this publication in new window or tab >>Friction Coefficients and Grasp Synthesis
2013 (English)In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), IEEE , 2013, 3520-3526 p.Conference paper (Refereed)
Abstract [en]

We propose a new concept called friction sensitivity which measures how susceptible a specific grasp is to changes in the underlying friction coefficients. 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. We describe how grasps with low friction sensitivity can be used when a robot has an uncertain belief about friction coefficients and study the statistics of grasp quality under changes in those coefficients. We also provide a parametric estimate for the distribution of grasp qualities and friction sensitivities for a uniformly sampled set of grasps.

Place, publisher, year, edition, pages
IEEE, 2013
Series
, IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858
Keyword
grasping, friction sensitivity, robotic manipulation
National Category
Computer Science Robotics
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-129498 (URN)10.1109/IROS.2013.6696858 (DOI)000331367403087 ()2-s2.0-84893771956 (ScopusID)978-146736358-7 (ISBN)
Conference
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013; Tokyo; Japan; 3 November 2013 through 8 November 2013
Funder
EU, FP7, Seventh Framework Programme, FP7-ERC-279933Swedish Research CouncilSwedish Foundation for Strategic Research
Note

QC 20140128

Available from: 2013-10-13 Created: 2013-09-30 Last updated: 2016-05-16Bibliographically approved
2. Combinatorial optimization for hierarchical contact-level grasping
Open this publication in new window or tab >>Combinatorial optimization for hierarchical contact-level grasping
2014 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2014, 381-388 p.Conference 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
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-176144 (URN)10.1109/ICRA.2014.6906885 (DOI)2-s2.0-84929171240 (ScopusID)
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
3. Hierarchical Fingertip Space for Multi-fingered Precision Grasping
Open this publication in new window or tab >>Hierarchical Fingertip Space for Multi-fingered Precision Grasping
2014 (English)In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE , 2014, 1641-1648 p.Conference 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
Series
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keyword
Contact levels, Experimental evaluation, Grasp synthesis, Hand kinematics, Hand manipulation, Hierarchical encoding, Multilevel refinement, Point cloud data
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-163508 (URN)10.1109/IROS.2014.6942775 (DOI)000349834601110 ()2-s2.0-84911484070 (ScopusID)978-1-4799-6934-0 (ISBN)
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
4. On the Evolution of Fingertip Grasping Manifolds
Open this publication in new window or tab >>On the Evolution of Fingertip Grasping Manifolds
Show others...
2016 (English)In: IEEE International Conference on Robotics and Automation, IEEE Robotics and Automation Society, 2016Conference paper (Refereed)
Abstract [en]

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. 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. The system is evaluated both in simulation and on a SchunkSDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system’s experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2016
Keyword
Fingertip Grasping, Grasping Manifold
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-187060 (URN)
Conference
IEEE International Conference on Robotics and Automation
Projects
RobDream
Note

QC 20160517

Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2016-05-18Bibliographically approved
5. Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
Open this publication in new window or tab >>Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
Show others...
2016 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468Article in journal (Refereed) Accepted
Abstract [en]

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. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.

Place, publisher, year, edition, pages
IEEE Press, 2016
Keyword
Hierarchical Fingertip Space, Grasp Planning, Grasp Adaptation, Fingertip Grasping
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-187058 (URN)
Projects
FlexBot
Funder
EU, European Research Council, FLEXBOT - FP7-ERC-279933
Note

QC 20160517

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

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