Add Lampros

Author: costashatz

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    **Abstract:** *Kinodynamic planning is a critical component of robotics, enabling robots to generate dynamically feasible trajectories while adhering to geometric constraints. Existing methods for kinodynamic planning primarily fall into two categories: (a) stochastic planners, such as Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), which excel in state-space exploration due to their directed randomness, and (b) optimization-based approaches, which are well-suited for structured environments, producing smooth and optimal solutions. In this work, we present Hybrid Trajectory Exploration for Kinodynamic Planning (HyTraX), a hybrid framework that integrates the strengths of these two paradigms. Specifically, HyTraX enhances the effectiveness of the Go-Explore algorithm by incorporating trajectory optimization with random target selection as its core exploration strategy. We evaluate HyTraX on two challenging kinodynamic motion planning tasks: a car-like agent and a planar quadrotor navigating through maze environments. Our results demonstrate significant performance improvements with minimal task-specific tuning, highlighting the robustness and versatility of the proposed approach.*
 
+   [(view online)](https://costashatz.github.io/files/ICARA2025.pdf)
+
 ### 5. Tsikelis, I.\*, and Chatzilygeroudis, K.\* 2024. **Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization**. *IEEE-RAS International Conference on Humanoid Robots (Humanoids).*
 
    **Abstract:** *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.*

src/publications.md

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 **Short bio:** Konstantinos Asimakopoulos received his Integrated Master's Diploma in Electrical & Computer Engineering in 2022 from the University of Patras, Greece. While he was an undergraduate student he also worked as a translator for academic books about machine learning and artificial intelligence for Fountasbooks. Through his master's thesis he explored how to use AI to generate art and in particular music composition. In 2023 he enrolled as a PhD candidate in the department of Electrical & Computer Engineering at the University of Patras researching physics informed reinforcement learning. His research interests include robot control, machine learning and how to combine ML with traditional control.
 
-**Website:** [https://github.com/konassimako](https://github.com/konassimako)
+![Lampros Printzios](images/printzios.png){: style="width:10%"}
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+**Short bio:** Lampros Printzios received his Integrated Master's Diploma in Electrical & Computer Engineering in 2024 from the University of Patras, Greece. Through his master's thesis he created a 3D printed robotic manipulator and developed several constrained path-planning algorithms for end-effector obstacle-free tracking. In 2024 he enrolled as a PhD candidate in the department of Electrical & Computer Engineering at the University of Patras researching accelerated methods for robot control. His research interests include robot control, machine learning and how to accelerate computations for fast robot control.
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+**Website:** [https://www.linkedin.com/in/lampros-printzios-ab74bb237](https://www.linkedin.com/in/lampros-printzios-ab74bb237)
 
 # Alumni