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On the Evolution of Fingertip Grasping Manifolds
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.
EPFL.
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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 [en]
Fingertip Grasping, Grasping Manifold
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-187060OAI: oai:DiVA.org:kth-187060DiVA: diva2:928843
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
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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