DocumentCode :
716755
Title :
Learning the tactile signatures of prototypical object parts for robust part-based grasping of novel objects
Author :
Hyttinen, Emil ; Kragic, Danica ; Detry, Renaud
Author_Institution :
Comput. Vision & Active Perception Lab., KTH R. Inst. of Technol., Stockholm, Sweden
fYear :
2015
fDate :
26-30 May 2015
Firstpage :
4927
Lastpage :
4932
Abstract :
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.
Keywords :
grippers; learning (artificial intelligence); pattern classification; pattern clustering; data-driven clustering process; gripper; object grasp stability; prototypical object parts; robotic agent; robust part-based object grasping; tactile signature learning; tactile-based stability model; touch-based stability classifier learning; touch-based stability estimators; Grasping; Joints; Prototypes; Robot sensing systems; Shape; Stability analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2015 IEEE International Conference on
Conference_Location :
Seattle, WA
Type :
conf
DOI :
10.1109/ICRA.2015.7139883
Filename :
7139883
Link To Document :
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