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Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects
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
2015 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2015, no June, 4927-4932 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a robotic agent that learns to derive object grasp stability from touch. The main contribution of our work is the use of a characterization of the shape of the part of the object that is enclosed by the gripper to condition the tactile-based stability model. As a result, the agent is able to express that a specific tactile signature may for instance indicate stability when grasping a cylinder, while cuing instability when grasping a box. We proceed by (1) discretizing the space of graspable object parts into a small set of prototypical shapes, via a data-driven clustering process, and (2) learning a touch-based stability classifier for each prototype. Classification is conducted through kernel logistic regression, applied to a low-dimensional approximation of the tactile data read from the robot's hand. We present an experiment that demonstrates the applicability of the method, yielding a success rate of 89%. Our experiment also shows that the distribution of tactile data differs substantially between grasps collected with different prototypes, supporting the use of shape cues in touch-based stability estimators.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. no June, 4927-4932 p.
Keyword [en]
Clustering algorithms, Stability, Clustering process, Data driven, Kernel logistic regression, Low-dimensional approximation, Object grasps, Part based, Robotic agents, Stability models, Robotics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-176113DOI: 10.1109/ICRA.2015.7139883ISI: 000370974904126Scopus ID: 2-s2.0-84938239973OAI: oai:DiVA.org:kth-176113DiVA: diva2:874754
Conference
2015 IEEE International Conference on Robotics and Automation, ICRA 2015, 26 May - 30 May 2015
Note

QC 20151127. QC 20160411

Available from: 2015-11-27 Created: 2015-11-02 Last updated: 2016-04-11Bibliographically approved

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Kragic, Danica

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