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Personal profile

Biography

Dana Kulić develops autonomous systems that can operate in concert with humans, using natural and intuitive interaction strategies while learning from user feedback to improve and individualize operation over long-term use. In collaboration with Prof. Elizabeth Croft, she pioneered systems to quantify and control safety during HRI based on both robot and human perception. Working with Prof. Yoshihiko Nakamura at the University of Tokyo, she developed one of the first systems to implement continuous learning from demonstration. The system was a first step towards robots that can learn from non-experts, as it did not require the demonstrator to segment or scaffold their demonstration. Her research in rehabilitation technology enables highly accurate, non-invasive, measurement of human movement, which can be deployed in industrial settings for accurate measurement of operator movement. She serves as the Global Innovation Research Visiting Professor at the Tokyo University of Agriculture and Technology, and the August-Wilhelm Scheer Visiting Professor at the Technical University of Munich. Before coming to Monash, Dr. Kulić established the Adaptive Systems Lab at the University of Waterloo, and collaborated with colleagues to establish Waterloo as one of Canada’s leading research centres in robotics. She is aco-Investigator of the Waterloo Robohub, the largest robotics experimental facility in Canada, and a co-Principal Investigator of the Natural Sciences and Engineering Research Council (NSERC) Canadian Robotics Network, Canada’s only federally funded network in robotics. She has led a number of large research projects and collaborations with industry and user groups, including a strategic project grant in collaborative assembly and multiple grants developing automation for rehabilitation.

Research area keywords

  • human-robot interaction
  • robot learning
  • human motion analysis

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2003 2019

A multi-layer Gaussian process for motor symptom estimation in people with Parkinson's Disease

Lang, M., Pfister, F. M. J., Frohner, J., Abedinpour, K., Pichler, D., Fietzek, U., Um, T. T., Kulic, D., Endo, S. & Hirche, S., 1 Nov 2019, In : IEEE Transactions on Biomedical Engineering. 66, 11, p. 3038-3049 12 p.

Research output: Contribution to journalArticleResearchpeer-review

Bayesian active learning for collaborative task specification using equivalence regions

Wilde, N., Kulic, D. & Smith, S. L. J., 1 Apr 2019, In : IEEE Robotics and Automation Letters. 4, 2, p. 1691-1698 8 p., 8633961.

Research output: Contribution to journalArticleResearchpeer-review

Creating personalized dynamic models

Venture, G., Bonnet, V. & Kulic, D., 2019, Biomechanics of Anthropomorphic Systems. Venture, G., Laumond, J-P. & Watier, B. (eds.). Cham Switzerland: Springer-Verlag London Ltd., p. 91-104 14 p. (Springer Tracts in Advanced Robotics; vol. 124).

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

Curiosity did not kill the robot: a curiosity-based learning system for a shopkeeper robot

Doering, M., Liu, P., Glas, D. F., Kanda, T., Kulic, D. & Ishiguro, H., Aug 2019, In : ACM Transactions on Human-Robot Interaction. 8, 3, 24 p., 15.

Research output: Contribution to journalArticleResearchpeer-review

Estimation and observability analysis of human motion on Lie groups

Joukov, V., Cesic, J., Westermann, K., Markovic, I. & Kulic, D., 26 Sep 2019, (Accepted/In press) In : IEEE Transactions on Cybernetics. 12 p.

Research output: Contribution to journalArticleResearchpeer-review