NDLI: Leveraging appearance priors in non-rigid registration, with application to manipulation of deformable objects

Content Provider	IEEE Xplore Digital Library
Author	Huang, S.H. Jia Pan Mulcaire, G. Abbeel, P.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Univ. of California at Berkeley, Berkeley, CA, USA (Huang, S.H.; Jia Pan; Mulcaire, G.; Abbeel, P.)
Abstract	Manipulation of deformable objects is a widely applicable but challenging task in robotics. One promising nonparametric approach for this problem is trajectory transfer, in which a non-rigid registration is computed between the starting scene of the demonstration and the scene at test time. This registration is extrapolated to find a function from $ℝ^{3}$ to $ℝ^{3},$ which is then used to warp the demonstrated robot trajectory to generate a proposed trajectory to execute in the test scene. In prior work [1] [2], only depth information from the scenes has been used to compute this warp function. This approach ignores appearance information, but there are situations in which using both shape and appearance information is necessary for finding high quality non-rigid warp functions. In this paper, we describe an approach to learn relevant appearance information about deformable objects using deep learning, and use this additional information to improve the quality of non-rigid registration between demonstration and test scenes. Our method better registers areas of interest on deformable objects that are crucial for manipulation, such as rope crossings and towel corners and edges. We experimentally validate our approach in both simulation and in the real world, and show that the utilization of appearance information leads to a significant improvement in both selecting the best matching demonstration scene for a given test scene, and finding a high quality non-rigid registration between those two scenes.
Starting Page	878
Ending Page	885
File Size	4181240
Page Count	8
File Format	PDF
e-ISBN	9781479999941
DOI	10.1109/IROS.2015.7353475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-28
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Robots Shape Machine learning Three-dimensional displays Neural networks Context
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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