Rudolf Lioutikov

Research Group Leader

Karlsruhe Insitute of Technology


Rudolf Lioutikov is a Research Group Leader at the Karlsruhe Institute of Technology. He started the Intuitive Robots Lab in June 2021 after being accepted into the Emmy Noether Programme by the German Research Foundation (Deutsche Forschungsgemeinschaft). The group develops new robot learning methods that focus on human-robot interaction with non-experts.

Previously Rudolf was an Assistant Professor of Practice at the University of Texas at Austin. He developed and taught the Robot Learning Stream of the Freshmen Research Initiative. Simultaneously Rudolf was a Postdoctoral Fellow with the Personal Autonomous Robotics Lab, where he developed new methods for areas such as robot learning, reinforcement learning, imitation learning and human-robot collaboration.

Before he joined the UT Computer Science Department, Rudolf worked as a Ph.D. candidate at the Intelligent Autonomous Systems Lab in Darmstadt. In his dissertation he developed an imitation learning pipeline which learns a library of movement primitives and a comprehensible behavior representation from unlabeled data.

Rudolf was warded his Ph.D. with distinction by the Technische Univeristät Darmstadt in 2018 and his dissertation was considered a finalist for the Georges Giralt PhD Award by the European Robotics Federation.


  • Intuitive Robots
  • Machine Learning
  • Robotics
  • Robot Learning
  • Imitation Learning
  • Human-Robot Collaboration

Academic Career

  • Research Group Leader

    Karlsruhe Insitute of Technology, since 06.2021

  • Assistant Professor of Practice

    University of Texas at Austin, 01.2019 - 05.2021

  • PhD in Machine Learning and Robotics

    Technische Universität Darmstadt, 10.2013 - 10.2018

Recent Publications

Distributional Depth-Based Estimation of Object Articulation Models

We propose a method that efficiently learns distributions over articulation models directly from depth images without the need to know …

ScrewNet: Category-Independent Articulation Model Estimation from Depth Images Using Screw Theory

Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and …

Self-Supervised Online Reward Shaping in Sparse-Reward Environments

We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in …


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