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A Framework For Optimal Grasp Contact Planning
Robotics, Perception, and Learning Lab, KTH Royal Institute of Technology, Stockholm, Sweden.
Robotics, Perception, and Learning Lab, KTH Royal Institute of Technology, Stockholm, Sweden. (AASS)ORCID iD: 0000-0003-3958-6179
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 704-711Article in journal (Refereed) Published
Abstract [en]

We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions under which minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.

Place, publisher, year, edition, pages
IEEE Press, 2017. Vol. 2, no 2, p. 704-711
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-71557DOI: 10.1109/LRA.2017.2651381ISI: 000413736600043OAI: oai:DiVA.org:oru-71557DiVA, id: diva2:1279859
Funder
EU, European Research CouncilKnut and Alice Wallenberg Foundation
Note

Equal contribution of first two authors. Selected for presentation at ICRA 2017.

Funding agency: National Science Foundation

Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-01-22Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
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  • apa
  • harvard1
  • ieee
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Language
  • de-DE
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  • Other locale
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Output format
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