![R.lioutikov](/img/rudi_lioutikov_rdax_230x230p.jpg)
Dr.-Ing. Rudolf Lioutikov
- Leitung Emmy Noether Gruppe
- Gruppe: Intuitive Robots Lab (IRL)
- Sprechstunden: nach Vereinbarung (by arrangement)
- Raum: Building 50.21 Room 209
- Tel.: +49 721 608-47106
- rudolf lioutikov ∂does-not-exist.kit edu
Adenauerring 4
76131 Karlsruhe
Rudolf Lioutikov
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.
An overview of Rudolf's articles and publications can be found on Google Scholar.
Intuitive Robot Intelligence: Efficiently Learning and Improving of Explainable Skills and Behaviors for Intuitive Human-Robot Interaction
- Ansprechperson:
Dr.-Ing. Rudolf Lioutikov
- Projektgruppe:
Emmy Noether
- Förderung:
DFG
- Starttermin:
02.03.2021
- Endtermin:
01.03.2024
Artificial intelligence approaches have produced impressive results across a wide spectrum of fields and applications in recent years. These successes in combination with an increasing demand for assisted living, elderly care and local production have have caused the expectation of an imminent deployment of intelligent autonomous robots in our everyday life. These future agents will be expected to work in close interaction with non-expert users in both general everyday situations and professional tasks. In either scenario such intelligent agents will have to adapt to new or modified tasks in dynamic environments without relying on the huge amounts of data that can not be provided by non-experts in either the quantity or quality required by current approaches. Furthermore, state-of-the-art machine learning methods are not able to represent the learned models, behaviors and features in a transparent and comprehensible way resulting in processes with insufficient guarantees and additional complexity for human-machine collaborations. A new generation of intelligent robots will be required that is capable of communicating intent to non-expert users as well as understanding intent from action of the user in a natural way. These robots will appear more intuitive to non-expert users as well as being able to deduct valuable information through more intuitive interaction with the non-expert user. This new generation of intelligent, intuitive robots will requirei) efficient learning of explainable and comprehensible skills and behaviors,ii) skill and behavior improvement from weakly labeled, suboptimal demonstrations,iii) efficient transfer and adaptation of skills and behaviors through intuitive interaction.This project will investigate these three aspect in various human-robot collaboration tasks.