Danica Kragic

Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. She chaired IEEE RAS Technical Committee on Computer and Robot Vision and served as an IEEE RAS AdCom member.  In 2012, she received an ERC Starting Grant. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council.

Her research explores how topological representations can be used for an integrated approach toward i) vision based understanding of complex human hand motion, ii) mapping and control of robotics hands based on the extracted knowledge, and iii) integrating the topological representations with models for high-level task encoding and task level planning. This research opens for new and important areas scientifically and technologically. Scientifically, it pushes for new way of thinking in an area that has traditionally been born from mechanics an modelling of bodies but not seeking for optimal design. Technologically, it provides methods plausible for evaluation of new designs of robotic and prosthetic hands. Further development of machine learning and computer vision methods allow for scene understanding that goes beyond the assumption of worlds of rigid bodies, including articulated and flexible objects.

One of just finished projects, RECONFIG:  Cognitive, Decentralized Coordination of Heterogeneous Multi-Robot Systems via Reconfigurable Task Planning aimed at exploiting recent developments in vision, robotics, and control to tackle coordination in heterogeneous multi-robot systems. Such systems hold promise for achieving robustness by leveraging upon the complementary capabilities of different agents and efficiency by allowing sub-tasks to be completed by the most suitable agent. A key challenge is that agent composition in current multi-robot systems needs to be constant and pre-defined. Moreover, the coordination of heterogeneous multi-agent systems has not been considered in manipulative scenarios. We proposed a reconfigurable and adaptive decentralized coordination framework for heterogeneous multiple and multi-DOF robot systems.