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Kitting in the Wild through Online Domain Adaptation
Sapienza Univ Rome, Rome, Italy.;Fdn Bruno Kessler, Trento, Italy..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0001-6671-9366
Fdn Bruno Kessler, Trento, Italy.;Univ Trento, Trento, Italy..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-1170-7162
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2018 (English)In: 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Maciejewski, AA Okamura, A Bicchi, A Stachniss, C Song, DZ Lee, DH Chaumette, F Ding, H Li, JS Wen, J Roberts, J Masamune, K Chong, NY Amato, N Tsagwarakis, N Rocco, P Asfour, T Chung, WK Yasuyoshi, Y Sun, Y Maciekeski, T Althoefer, K AndradeCetto, J Chung, WK Demircan, E Dias, J Fraisse, P Gross, R Harada, H Hasegawa, Y Hayashibe, M Kiguchi, K Kim, K Kroeger, T Li, Y Ma, S Mochiyama, H Monje, CA Rekleitis, I Roberts, R Stulp, F Tsai, CHD Zollo, L, IEEE , 2018, p. 1103-1109Conference paper, Published paper (Refereed)
Abstract [en]

Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1103-1109
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-246309DOI: 10.1109/IROS.2018.8593862ISI: 000458872701034Scopus ID: 2-s2.0-85063002869ISBN: 978-1-5386-8094-0 (print)OAI: oai:DiVA.org:kth-246309DiVA, id: diva2:1297386
Conference
25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 01-05, 2018, Madrid, SPAIN
Note

QC 20190319

Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-05-16Bibliographically approved

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Karaoǧuz, HakanJensfelt, Patric

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