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TU Wien
Automation and Control Institute

 Contact data
StreetGusshausstr. 27/376 
ZIP, City, Region 1040 - Wien (Vienna) 
EmailSend Email
 Contact person

Markus Vincze (Professor of Robotics)
 Employees (Total)
Total: 51-250  number of researches: 80

 Central aim of organisation
R&D in Robotics, Machine Vision and Control

 Briefly describe your organisation
Technische Universit¨at Wien (www.tuwien.ac.at), is located in the heart of Europe,
in a cosmopolitan city of great cultural diversity. For more than 200 years, TU Wien has been a place of
research, teaching and learning in the service of progress. TU Wien is among the most successful technical universities
in Europe and is Austrias largest scientific-technical research and educational institution.
TUW is represented by the Automation and Control Institute (www.acin.tuwien.ac.at), which employs 86 persons
most of them researchers graduated in the fields of Electrical and Mechanical Engineering, Mathematics, and Computer Science. TUW-ACIN strongly emphasises the close co-operation with industry and 80% of the scientific employees are funded by national, European, and American industry or research projects. TUW-ACIN conducts research in the areas of automation technology, control theory, machine vision, and cognitive robotics. Specific emphasis of research work is on complex dynamic systems, non-linear control, agile manufacturing, ontologies for automation, and vision for robotics and cognitive systems.The Vision for Robotics Group belongs to the Automation and Control Institute of the Faculty of Electrical Engineering and Information Technology, Vienna University of Technology (VUT). V4R (http://www.acin.tuwien.ac.at/forschung/v4r/) conducts basic research in the field of computer vison for robotics and automation, cognitive robotics and robotics in education. We aim at applying the latest knowledge and advanced methods to solve challenging practical problems in close collaboration with industry. Among our research partners are well-known international and national enterprises that take advantage of both our expertise in research and consultation.

 Core Competency

Research & Development

 ICT sectors:
   - Video Processing     
   - Pattern Processing     
   - Automation     
   - Robotics     
   - Man-Machine Interaction     
   - Artificial Intelligence     
   - Sensor Technology     

 ICT keywords
robot vision, service robots, home robots, industrial robots, perception
 RTD Experiences & Publications

 My organisation has been involved in projects funded by the following EU-programmes
  Coordinator  Partner 
EU Framework Programme (FP)
Competitiveness & Innovation Programme (CIP)
Ambient Assisted Living (Art. 169)
Other international co-operative research

 Austrian Research and Development Projects
  Coordinator  Partner 
Non-cooperative Projects (Single projects)        
Cooperative Projects
Education Projects (Thesis, ..)

 RTD Experience
The Vision for Robotics team investigates methods to make robots see. We devise machine vision methods to perceive the scene and objects such that robots understand what they perceive, act in their environment, and keep learning from everyday situations. This paves the way to automated manufacturing and robots performing household tasks. Core expertise is safe navigation, 2D and 3D attention, object modelling, detection and classification, and manipulation of objects in relation to object functions.
V4R works with many different robots ranging from robot arms such as ABB and Kuka LWR over mobile robots from Metralabs such as Werner for professional care facilities and HOBBIT, a service robot for older adults at home, to humanoids such as Nao, Pepper and Romeo (all from Softbank – Aldebaran). For all these robots we develop methods to make robots detect, model, recognise, classify and track objects. We are particularly focused on real world scenarios.

[1] D. Fischinger, A. Weiss, M. Vincze: ”Learning Grasps with Topographic Features”; International Journal of Robotics Research 234(3), pp.12-35, 2015.
[2] Aldoma, A., Tombari, F., Di Stefano, L., Vincze, M.: A Global Hypothesis Verification Framework for 3D Object Recognition in Clutter; IEEE T-PAMI 38 (7):1383-1396, 2016.
[3] Thomas Fäulhammer, Rares Ambrus, Chris Burbridge, Michael Zillich, John Folkesson, Nick Hawes, Patric Jensfelt, Markus Vincze: Autonomous Learning of Object Models on a Mobile Robot; IEEE Robotics and Automation Letters 21), pp.26-33, 2016.
[4] T. Patten, M. Zillich, R. Fitch, M. Vincze, S. Sukkarieh: Viewpoint Evaluation for Online 3-D Active Object Classification; Robotics and Automation Letters (IEEE Journal), 1(1):73-81, 2016.
[5] Mörwald, T., Balzer, J., Vincze, M.: ”Modeling connected regions in arbitrary planar point clouds by robust B-spline approximation”; Robotics and Autonomous Systems, 76, pp. 141-151, 2016.
[6] M. Bajones, D. Fischinger, A. Weiss, D. Wolf, M. Vincze et al.: Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World; Journal of Robotics, 2018.
[7] N. Hawes, T. Fäulhammer, M. Zillich, M. Vincze, A. Aldoma Buchaca et al.: The STRANDS Project: Long-Term Autonomy in Everyday Environments; IEEE Robotics & Automation Magazine, 24 (2017), 3; S. 146 - 156.
[8] M. Loghmani, B. Caputo, M. Vincze: Recognizing Objects In-the-wild: Where Do We Stand?; arXiv.org Cornell University Library, 9 (2017), 8 S.
[9] M. Vincze: Die langsame Transformation der Robotik; E&I Elektrotechnik und Informationstechnik, 134 (2017), 7; S. 355 - 360.
[10] M. Vincze: Vertrauenswürdige Roboter; Elektrotechnik und Informationstechnik (e&i), 134 (2017), 6; S. 291 - 292.

 Inserted / Updated
2018-06-28 / 2018-06-28