If you made any changes in Pure these will be visible here soon.

Personal profile


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.


  • 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 2004 2019

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

Online learning of gait models from older adult data

Waugh, J. L. S., Huang, E., Fraser, J. E., Beyer, K. B., Trinh, A., McIlroy, W. E. & Kulić, D., 1 Apr 2019, In : IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27, 4, p. 733-742 10 p., 8665976.

Research output: Contribution to journalArticleResearchpeer-review

Stable Gaussian process based tracking control of Euler–Lagrange systems

Beckers, T., Kulić, D. & Hirche, S., 1 May 2019, In : Automatica. 103, p. 390-397 8 p.

Research output: Contribution to journalArticleResearchpeer-review

A spinal motion measurement protocol utilizing inertial sensors without magnetometers

Samadani, A., Lee, A. & Kulic, D., 26 Oct 2018, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. IEEE, Institute of Electrical and Electronics Engineers, p. 1-4 4 p. 8512565. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther