Robotics and Biology Laboratory

Oussama Zenkri


Office MAR 5-1
Room MAR 5.065
Office HoursOnly by appointment

Research Interests

Inspired by the remarkable rationality displayed by living organisms even under resource limitations, my research endeavors to uncover the underlying mechanisms of this intelligent behavior. This exploration not only promises to broaden our Psychological and Biological understanding, but it also holds the potential to revolutionize the field of robotics by creating systems that surpass current capabilities.

My focus is on studying the sophisticated problem-solving and strategy-learning techniques employed by humans, with the aim of integrating this knowledge into the design of more advanced and reliable robotic systems.

Short CV

Oct. 2021 - Present: 
Ph.D. Student, Robotics and Biology Lab, TU Berlin (Oliver Brock)
Member of the cluster of excellence Science of Intelligence

Apr. 2018 - Sep. 2021: 
M.Sc. Electrical Engineering and Information Technology - KIT, Karlsruhe
Master Thesis: Autonomous Learning Robots Lab (ALR) - KIT
Topic: Policy Learning for Grasping in a Heap

  • May 2019 - Mar. 2020
    • Working Student at the Fraunhofer Institute for Chemical Technology (ICT), Karlsruhe
  • Apr. 2019 - June 2020
    • Student Assistant at the Intitute for Anthropomatic and Robotic (IAR), KIT
  • Sep. 2018 - Feb. 2019
  • Apr. 2018 - Aug. 2018
    • Working Student at the Fraunhofer Institute for Chemical Technilogy (ICT), Karlsruhe

Oct. 2013 - Mar. 2018:
B.Sc. Electrical Engineering and Information Technology - KIT, Karlsruhe
Bachelor Thesis: Institute for Industrial Information Technology (IIIT) - KIT
Topic: Material Proportion Estimation through Evaluation of Hyperspectral Images



© DALL·E 2

Rational Selection of Exploration Strategies in an Escape room Task

How do humans select the right strategy to solve a task? We aim to unlock the secrets of the mind's toolbox. In this project, we explore the mechanisms behind strategy selection in solving cognitive and behavioral tasks. Focusing on how the trade-off between accuracy and costs is inferred, we aim to provide a deeper understanding of the ecologically rational strategy selection process and how it can be improved.