AI in Dynamic Action
About us...
The AI in dynamic action (AIDA) scientific group is dedicated to the study and application of artificial intelligence in dynamical environments, with a special focus on safety and trustworthiness. Among various machine learning approaches, we are especially interested in reinforcement learning - a methodology resembling the action of living beings in changing, uncertain environments which react by punishment and reward. Applied to human economy, AI has to fulfill requirements on safety and trustworthiness, especially regarding the personal data privacy. These requirements become particularly challenging in dynamical application of AI, such as robotic, autonomous driving, medical therapy support, chemical engineering, energy management etc.
The scientific group seeks to apply an interdisciplinary approach by the fusion of machine learning with various fields, such as system and control theory, to develop novel dynamic AI methods.
The team of the scientific group
Assistant Professor
Head of scientific group
Head of scientific group

Georgiy Malania
Junior Research Scientist

Ilya Osokin
Junior Research Scientist

Vadim Artyomov
MSc student

Vyacheslav Kovalev
MSc student

Mikhail Patrikeev
Engineer

Kirill Myasoyedov
Research Intern

Ivan Apanasevich
Engineer

Grigory Yaremenko
PhD student
Projects & research

Computer vision system for Advanced Driver Assistance Systems (ADAS).
We develop and test algorithms based on computer vision for road lane recognition and driver warning, prevention of leaving a virtual corridor is integrated as well.
Control algorithms for stable locomotion of quadrupedal robots.
We are develop algorithms for stable locomotion of leg robots based on model predictive control and reinforcement learning with special stabilizing machinery. This will allow stable walking especially walking up and down stairs of legged robotic platforms for better usability in civil, and Industrial environments.
Design, assembling implementation of a Greenhouse mobile robot for tomato monitoring.
We develop and deploy a Greenhouse robot with computer systems and data processing systems on board. This systems enable the robot to navigate between rows and capable of monitoring tomato condition.Publications
Beckenbach, P. Osinenko and S. Streif.
A Q-learning predictive control scheme with guaranteed stability.
European Journal of Control 56 (2020): 167-178
Beckenbach, P. Osinenko and S. Streif.
On closed-loop stability of model predictive controllers with learning costs.
European Control Conference, 2020
Osinenko, A. Kobelski and S. Streif.
A method of online traction parameter identification and mapping.
IFAC World Congress, 2020
Osinenko, L. Beckenbach, T. Göhrt and S. Streif.
A reinforcement learning method with real-time closed-loop stability guarantee.
IFAC World Congress, 2020
NO INFORusswurm, P. Osinenko and S. Streif.
Optimal control of centrifugal spreader.
IFAC World Congress, 2020