Prof. Dr. Majid Khadiv


AI Planning in Dynamic Environments

Academic Career and Research Areas

Prof. Majid Khadiv's (*1988) research interests include planning, control and learning for robotic systems, with an emphasis on locomotion and manipulation. His research goal is to develop theoretical frameworks that enable a loco-manipulation system to autonomously interact with the environment and continuously learn from these interactions. Experimental validation of developed algorithms on real robots is also an important axis of his research.

Prof. Khadiv received his PhD from K. N. Toosi University of Technology in 2017. During his PhD, he led for three years the dynamics and control team in the Iranian national humanoid robotics project, Surena III, and also visited for one year the Max Planck Institute for Intelligent Systems (MPI-IS). From 2018-2023, he was a research scientist at the MPI-IS. In September 2023, Prof. Khadiv was appointed to the professorship for AI Planning in Dynamic Environments at TUM and the Munich Institute of Robotics and Machine Intelligence (MIRMI).


  • Outstanding Reviewer Award, IEEE Robotics and Automation Letters (2023)
  • Best Paper Award, IEEE-RAS Technical Committee on Model-based optimization for robotics (2022)
  • 4x Internal Max Planck institute grants (Grassroots) (2019-2022)

Meduri, A., Shah, P., Viereck, J., Khadiv, M., Havoutis, I., & Righetti, L.: "Biconmp: A nonlinear model predictive control framework for whole body motion planning". IEEE Transactions on Robotics. 2023; 39(2): 905-922.


Yeganegi, M. H., Khadiv, M., Del Prete, A., Moosavian, S. A. A., & Righetti, L.: "Robust walking based on MPC with viability guarantees". IEEE Transactions on Robotics. 2021; 38(4): 2389-2404.


Ponton, B., Khadiv, M., Meduri, A., & Righetti, L.: "Efficient multicontact pattern generation with sequential convex approximations of the centroidal dynamics". IEEE Transactions on Robotics, 2021; 37(5): 1661-1679.


Khadiv, M., Herzog, A., Moosavian, S. A. A., & Righetti, L.: "Walking control based on step timing adaptation". IEEE Transactions on Robotics. 2020; 36(3): 629-643.


Bogdanovic, M., Khadiv, M., & Righetti, L.: "Learning variable impedance control for contact sensitive tasks". IEEE Robotics and Automation Letters. 2020; 5(4): 6129-6136.