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Learning Tactile Characterizations Of Object- And Pose-specific Grasps
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.
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
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Our aim is to predict the stability of a grasp from the perceptions available to a robot before attempting to lift up and transport an object. The percepts we consider consist of the tactile imprints and the object-gripper configuration read before and until the robot’s manipulator is fully closed around an object. Our robot is equipped with multiple tactile sensing arrays and it is able to track the pose of an object during the application of a grasp. We present a kernel-logistic-regression model of pose- and touch-conditional grasp success probability which we train on grasp data collected by letting the robot experience the effect on tactile and visual signals of grasps suggested by a teacher, and letting the robot verify which grasps can be used to rigidly control the object. We consider models defined on several subspaces of our input data – e.g., using tactile perceptions or pose information only. Our experiment demonstrates that joint tactile and pose-based perceptions carry valuable grasp-related information, as models trained on both hand poses and tactile parameters perform better than the models trained exclusively on one perceptual input.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011. 1554-1560 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keyword [en]
Grasping, Kernel Logistic Regression, Tactile Sensing
National Category
Computer Science Robotics
Identifiers
URN: urn:nbn:se:kth:diva-38280DOI: 10.1109/IROS.2011.6094878ISI: 000297477501137Scopus ID: 2-s2.0-84455203892ISBN: 978-1-61284-454-1 (print)OAI: oai:DiVA.org:kth-38280DiVA: diva2:436513
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems
Projects
EU FP7 project CogX
Funder
ICT - The Next Generation
Note
QC 20120403Available from: 2011-08-23 Created: 2011-08-23 Last updated: 2012-06-13Bibliographically approved

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Kragic, Danica

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