Update Oct 2025

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+## 24th October 2024
+
+Our paper **"RobotDART: a versatile robot simulator for robotics and machine learning researchers"** by *Chatzilygeroudis, K., Totsila, D., and Mouret J.-B.* has been accepted at the *[Journal of Open Source Software (JOSS)](https://joss.theoj.org/)*. More information on our paper [here](publications.md). Read more about our library [here](https://nosalro.github.io/robot_dart/).
+
+## 10th September 2024
+
+Our paper **"Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization"** by *Tsikelis, I., and Chatzilygeroudis, K.* has been accepted for presentation at the *[IEEE-RAS International Conference on Humanoid Robots (Humanoids)](https://2024.ieee-humanoids.org/)*. More information on our paper [here](publications.md). See you at Nancy in November 2024!
+
 ## 22th April 2024
 
 Our short paper **"Effective Kinodynamic Planning and Exploration through Quality Diversity and Trajectory Optimization"** by *Asimakopoulos, K., Androutsopoulos, A., Vrahatis, M. and Chatzilygeroudis, K.* has been accepted for presentation at the *international conference [LION18](https://www.lion18.unina.it/)*. More information on our paper [here](publications.md). The following presentation-only abstracts have been accepted as well: 1) **"Effective Skill Learning via Autonomous Goal Representation Learning"** by *C. Tsakonas and K. Chatzilygeroudis*, and 2) **"Evolving Dynamic Locomotion Policies in Minutes"** by *K. Chatzilygeroudis, C. Tsakonas and M. Vrahatis*! See you at Ischia in June 2024!

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+## International Journals
+
+### 1. Chatzilygeroudis, K., Dionis, T., Mouret, J.-B. 2024. **RobotDART: a versatile robot simulator for robotics and machine learning researchers**. *Journal of Open Source Software (JOSS).*
+
+   **Abstract:** *Robot simulation plays a pivotal role in robotics and machine learning research, offering a cost-effective and safe means to develop, validate, and benchmark algorithms in various scenarios. With the growing complexity of robotic systems and the increasing demand for data-driven approaches in machine learning, there is a pressing need for versatile and efficient robot simulators that cater to the diverse requirements of researchers. In response to this demand, we introduce RobotDART, a high-performance and versatile robot simulator designed to empower researchers in robotics and machine learning with a powerful and flexible simulation environment.*
+
+   [(view online)](https://joss.theoj.org/papers/10.21105/joss.06771)
+   [(documentation)](https://nosalro.github.io/robot_dart/)
+   [(code)](https://github.com/NOSALRO/robot_dart)
+   [(doi)](https://doi.org/10.21105/joss.06771)
+
 ## International Conferences
 
+### 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.*
+
+   *\* Equal Contribution*
+
+   [(view online)](https://arxiv.org/abs/2410.02891)
+
 ### 4. Asimakopoulos, K., Androutsopoulos, A., Vrahatis, M. and Chatzilygeroudis, K. 2024. **Effective Kinodynamic Planning and Exploration through Quality Diversity and Trajectory Optimization**. *The 18th learning and intelligent optimization conference (LION).*
 
    **Abstract:** *Efficient and rapid kinodynamic planning is crucial for numerous real-world robotics applications. Various methods have been proposed to address this challenge, primarily falling into two categories: (a) randomized planners and (b) trajectory optimization utilizing simplified models and numerical optimization. Randomized planners such as RRT and PRM excel in exploring the state space, while trajectory optimization methods, like direct collocation, are adept at discovering optimal trajectories within well-defined spaces. We aim to achieve effective and efficient kinodynamic planning and exploration by integrating evolutionary algorithms (Quality-Diversity) with trajectory optimization. Our preliminary experiments showcase that using the proposed methodology we get the best from both worlds on two simulated experiments.*