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Publications (3 of 3) Show all publications
Kragic, D., Gustafson, J., Karaoǧuz, H., Jensfelt, P. & Krug, R. (2018). Interactive, collaborative robots: Challenges and opportunities. In: IJCAI International Joint Conference on Artificial Intelligence: . Paper presented at 27th International Joint Conference on Artificial Intelligence, IJCAI 2018; Stockholm; Sweden; 13 July 2018 through 19 July 2018 (pp. 18-25). International Joint Conferences on Artificial Intelligence
Open this publication in new window or tab >>Interactive, collaborative robots: Challenges and opportunities
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2018 (English)In: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2018, p. 18-25Conference paper, Published paper (Refereed)
Abstract [en]

Robotic technology has transformed manufacturing industry ever since the first industrial robot was put in use in the beginning of the 60s. The challenge of developing flexible solutions where production lines can be quickly re-planned, adapted and structured for new or slightly changed products is still an important open problem. Industrial robots today are still largely preprogrammed for their tasks, not able to detect errors in their own performance or to robustly interact with a complex environment and a human worker. The challenges are even more serious when it comes to various types of service robots. Full robot autonomy, including natural interaction, learning from and with human, safe and flexible performance for challenging tasks in unstructured environments will remain out of reach for the foreseeable future. In the envisioned future factory setups, home and office environments, humans and robots will share the same workspace and perform different object manipulation tasks in a collaborative manner. We discuss some of the major challenges of developing such systems and provide examples of the current state of the art.

Place, publisher, year, edition, pages
International Joint Conferences on Artificial Intelligence, 2018
Keywords
Artificial intelligence, Industrial robots, Collaborative robots, Complex environments, Manufacturing industries, Natural interactions, Object manipulation, Office environments, Robotic technologies, Unstructured environments, Human robot interaction
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247239 (URN)2-s2.0-85055718956 (Scopus ID)9780999241127 (ISBN)
Conference
27th International Joint Conference on Artificial Intelligence, IJCAI 2018; Stockholm; Sweden; 13 July 2018 through 19 July 2018
Funder
Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20190402

Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-05-22Bibliographically approved
Mancini, M., Karaoǧuz, H., Ricci, E., Jensfelt, P. & Caputo, B. (2018). Kitting in the Wild through Online Domain Adaptation. In: 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 (Ed.), 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS): . Paper presented at 25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OCT 01-05, 2018, Madrid, SPAIN (pp. 1103-1109). IEEE
Open this publication in new window or tab >>Kitting in the Wild through Online Domain Adaptation
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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
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-246309 (URN)10.1109/IROS.2018.8593862 (DOI)000458872701034 ()2-s2.0-85063002869 (Scopus ID)978-1-5386-8094-0 (ISBN)
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
Karaoǧuz, H. & Işil Bozma, H. (2016). Merging appearance-based spatial knowledge in multirobot systems. In: IEEE International Conference on Intelligent Robots and Systems: . Paper presented at 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016 (pp. 5107-5112). IEEE
Open this publication in new window or tab >>Merging appearance-based spatial knowledge in multirobot systems
2016 (English)In: IEEE International Conference on Intelligent Robots and Systems, IEEE, 2016, p. 5107-5112Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the merging of appearancebased spatial knowledge among robots having compatible visual sensing. Each robot is assumed to retain its knowledge in its individual long-term spatial memory where i) the place knowledge and their spatial relations are retained in an organized manner in place and map memories respectively; and ii) a 'place' refers to a spatial region as designated by a collection of associated appearances. In the proposed approach, each robot communicates with another robot, receives its memory and then merges the received knowledge with its own. The novelty of the merging process is that it is done in two stages: merging of place knowledge followed by the merging of map knowledge. As each robot's place memory is processed as a whole or in portions, the merging process scales easily with respect to the amount and overlap of the appearance data. Furthermore, the merging can be done in decentralized manner. Our experimental results with a team of three robots demonstrate that the resulting merged knowledge enables the robots to reason about learned places.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Intelligent robots, Robots, Viscosity measurement, Appearance based, Merging process, Multi-robot systems, Spatial knowledge, Spatial memory, Spatial regions, Spatial relations, Visual sensing, Merging
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-202117 (URN)10.1109/IROS.2016.7759749 (DOI)000391921705020 ()2-s2.0-85006380155 (Scopus ID)9781509037629 (ISBN)
Conference
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016
Note

QC 20170301

Available from: 2017-03-01 Created: 2017-03-01 Last updated: 2017-08-15Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6671-9366

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