Ioannis Tsikelis

615 Rue du Jardin-Botanique

54600 Villers-lès-Nancy

France

I am currently a Doctoral Researcher at Inria Centre at Université de Lorraine (LARSEN Team) under the supervision of Dr. Enrico Mingo-Hoffman. My research work focuses on combining model-based methods and machine learning for robotic motion planning and control.

I graduated with a Diploma (Integrated MSc) from the Computer Engineering and Informatics Department at the University of Patras, Greece in June 2024. My diploma thesis, entitled "Motion Planning of Robotic Systems Using Numerical Methods" was co-supervised by Prof. Emmanouil Psarakis and Prof. Konstantinos Chatzilygeroudis and involved generating complex trajectories for dynamical systems by formulating the motion planning task as a numerical optimisation problem.

As a student, I was a member of the University of Patras Robotics Club, an interdisciplinary team of students aimed at designing and constructing small and medium size robots for various competitive scenarios. Working with the team provided me with useful insight on all aspects of robotic system design. Our robots have been awarded numerous times internationally.

selected publications

Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization

Ioannis Tsikelis^*, and Konstantinos Chatzilygeroudis^*

In IEEE-RAS International Conference on Humanoid Robots (Humanoids) , 2024

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Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems.

To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling.
@inproceedings{tsikelis2024gait, title = {Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization}, author = {Tsikelis, Ioannis and Chatzilygeroudis, Konstantinos}, booktitle = {IEEE-RAS International Conference on Humanoid Robots (Humanoids)}, year = {2024}, dimensions = {true}, }