Adaptive Grasping Using Tactile Sensing

Tid: Må 2017-06-05 kl 13.00

Plats: Room 304, Teknikringen 14, KTH Campus

Abstract

Grasping novel objects is challenging because of incomplete object data and because of uncertainties inherent in real world applications. To robustly perform grasps on previously unseen objects, feedback from touch is essential. In our research, we study how information from touch sensors can be used to improve grasping novel objects. Since it is not trivial to extract relevant object properties and deduce appropriate actions from touch sensing, we employ machine learning techniques to learn suitable behaviors. We have shown that grasp stability estimation based on touch can be improved by including an approximate notion of object shape. Further we have devised a method to guide local grasp adaptations based on our stability estimation method. Grasp corrections are found by simulating tactile data for grasps in the vicinity of the current grasp. We present several experiments to demonstrate the applicability of our methods. The thesis is concluded by discussing our results and suggesting potential topics for further research.

The thesis in Diva

Ämnesområde: Computer vision and robotics

Respondent: Emil Hyttinen , RPL

Opponent: Dr. Giacomo Spampinato

Handledare: Professor Danica Kragic

2017-06-05T13:00 2017-06-05T13:00 Adaptive Grasping Using Tactile Sensing (Computer vision and robotics) Adaptive Grasping Using Tactile Sensing (Computer vision and robotics)
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