Task-specific grasping of similar objects by probabilistic fusion of vision and tactile measurements

Ekaterina Kolycheva (née Nikandrova), Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

This paper presents a probabilistic approach for task-specific grasping of novel objects from a known category. RGB-D imaging is used to establish an initial estimate of the target object's shape and pose, which is used to plan an optimal grasp over the uncertain estimate. Tactile information is then used for incrementally improving the estimate and sequentially replanning better grasps. The resulting grasp is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context. Experimental results in simulation and on a real platform show that tactile information can be used for improving the stability of grasps for objects which belong to a known category even if they vary considerably in shape.

Original languageEnglish
Title of host publicationIEEE-RAS International Conference on Humanoid Robots
Place of PublicationSeoul, Korea
PublisherIEEE Computer Society
Pages704-710
Number of pages7
Volume2015-December
ISBN (Electronic)978-1-4799-6884-8
ISBN (Print)9781479968855
DOIs
Publication statusPublished - 22 Dec 2015
MoE publication typeA4 Article in a conference publication
EventIEEE-RAS International Conference on Humanoid Robots - Seoul, Korea, Republic of
Duration: 3 Nov 20155 Nov 2015
Conference number: 15

Conference

ConferenceIEEE-RAS International Conference on Humanoid Robots
Abbreviated titleHumanoids
CountryKorea, Republic of
CitySeoul
Period03/11/201505/11/2015

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