Nemanja Rakicevic obtained his BSc degree in Mechatronics at the Faculty of Technical Sciences, University of Novi Sad in 2011. He completed the double-degree EMARO (European Masters on Advanced Robotics) program and was awarded the MSc degree in 2013.
During 2013-2014, he worked as a research engineer at RIS group, LAAS-CNRS Toulouse on rover locomotion diagnostics using sequential machine learning models.
From 2015 to 2016, he was as a research assistant at iBug, Dept. of Computing, ICL where he worked on applying deep learning methods for human emotion recognition based on facial expressions.
He started his PhD in 2016 in the Robot Intelligence Lab, Dyson School of Design Engineering, ICL.
His research interests lie at the intersection of artificial intelligence and robotics, more specifically representation learning, policy search and deep reinforcement learning for continuous control.
et al., 2021, Hierarchical decomposed-objective model predictive control for autonomous casualty extraction, Ieee Access, Vol:9, ISSN:2169-3536, Pages:39656-39679
Rakicevic N, Kormushev P, 2019, Active learning via informed search in movement parameter space for efficient robot task learning and transfer, Autonomous Robots, Vol:43, ISSN:0929-5593, Pages:1917-1935
Rakicevic N, Cully A, Kormushev P, Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution, Proceedings of the 2021 Genetic and Evolutionary Computation Conference
Saputra RP, Rakicevic N, Kormushev P, 2020, Sim-to-real learning for casualty detection from ground projected point cloud data, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE