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src/news.md

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+## 14th December 2024
+
+Our paper **"Sensorimotor Learning with Stability Guarantees via Autonomous Neural Dynamic Policies"** by *Dionis, T., Chatzilygeroudis, K., Modugno, V., Hadjivelichkov, D. and Kanoulas, D.* has been accepted at the *[IEEE Robotics and Automation Letters (RA-L)](https://www.ieee-ras.org/publications/ra-l)*. More information on our paper [here](publications.md).
+
 ## 26th November 2024
 
 Our paper **"Integrating Trajectory Optimization in Quality-Diversity for Kinodynamic Motion Planning"** by *Asimakopoulos, K., and Chatzilygeroudis, K.* has been accepted at the *[11th International Conference on Automation, Robotics, and Applications (ICARA 2025)](https://icara.us/)*. More information on our paper [here](publications.md). See you at Zagred in February 2025!

src/publications.md

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 ## International Journals
 
+### 2. Dionis, T.\*, Chatzilygeroudis, K.\*, Modugno, V., Hadjivelichkov, D. and Kanoulas, D. 2024. **Sensorimotor Learning with Stability Guarantees via Autonomous Neural Dynamic Policies**. *IEEE Robotics and Automation Letters (RA-L).*
+
+   **Abstract:** *State-of-the-art sensorimotor learning algorithms, either in the context of reinforcement learning or imitation learning, offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Moreover, it is very difficult to interpret the optimized controller and analyze its behavior and/or performance. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be analyzed for stability/safety. Such policies, however, are neither flexible nor generic and usually work only with proprioceptive sensor states. In this work, we bridge the gap between generic neural network policies and dynamical system-based policies, and we introduce Autonomous Neural Dynamic Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b) always produce asymptotically stable behaviors, and (c) are more flexible than traditional stable dynamical system-based policies. ANDPs are fully differentiable, flexible generic-policies that accept any observation input, while ensuring asymptotic stability. Through several experiments, we explore the flexibility and capacity of ANDPs in several imitation learning tasks including experiments with image observations. The results show that ANDPs combine the benefits of both neural network-based and dynamical system-based methods.*
+
+   *\* Equal Contribution*
+
+   [(view online)](https://nosalro.github.io/andps)
+   [(code)](https://github.com/NOSALRO/andps)
+
 ### 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.*

src/team.md

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 # Research Team
 
-## Constantinos Tsakonas
-
-![Constantinos Tsakonas](images/ct.jpg){: style="width:10%"}
-
-**Short bio:** Constantinos Tsakonas received his diploma from Computer Engineering and Informatics Department (CEID) from the University of Patras, Greece in 2023. He worked at the IoT Laboratory of the University of Patras as a machine learning engineer and researcher, with a focus on TinyML and AI-enabled embedded systems (2022-2023). He has also participated to Pfizer’s CDI project, "Voice-based Diagnostics" (direct contract with IoT Laboratory), as a machine learning researcher where he worked on neural network architectures for audio data and state-of-the-art digital signal processing techniques. He co-authored two research papers published on ACM Embedded Networked Sensor Systems (SenSys 2022) and a journal published on MDPI Sensors. Additionally, he is a reviewer on conferences considering Artificial Intelligence and Robotics, such as Learning and Intelligent Optimization Conference (LION 2023) and the International Conference on Intelligent Robots and Systems (IROS 2023). Moreover, he has won the local round of a European machine learning hackathon, where the problem formulation and solution review was carried out by Ernst & Young (EY). And later on, he represented the University of Patras at the final round in Milan, IT, competing in a challenge provided by Infineon Technologies. His research interests include artificial intelligence, evolutionary algorithms, optimization, and robot learning.
-
-**Website:** [https://github.com/iamtsac](https://github.com/iamtsac)
-
 ## Konstantinos Asimakopoulos
 
 ![Konstantinos Asimakopoulos](images/ka.jpg){: style="width:10%"}
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 **Website:** [https://github.com/konassimako](https://github.com/konassimako)
 
+# Alumni
+
+## Constantinos Tsakonas
+
+![Constantinos Tsakonas](images/ct.jpg){: style="width:10%"}
+
+**Short bio:** Constantinos Tsakonas received his diploma from Computer Engineering and Informatics Department (CEID) from the University of Patras, Greece in 2023. He worked at the IoT Laboratory of the University of Patras as a machine learning engineer and researcher, with a focus on TinyML and AI-enabled embedded systems (2022-2023). He has also participated to Pfizer’s CDI project, "Voice-based Diagnostics" (direct contract with IoT Laboratory), as a machine learning researcher where he worked on neural network architectures for audio data and state-of-the-art digital signal processing techniques. He co-authored two research papers published on ACM Embedded Networked Sensor Systems (SenSys 2022) and a journal published on MDPI Sensors. Additionally, he is a reviewer on conferences considering Artificial Intelligence and Robotics, such as Learning and Intelligent Optimization Conference (LION 2023) and the International Conference on Intelligent Robots and Systems (IROS 2023). Moreover, he has won the local round of a European machine learning hackathon, where the problem formulation and solution review was carried out by Ernst & Young (EY). And later on, he represented the University of Patras at the final round in Milan, IT, competing in a challenge provided by Infineon Technologies. His research interests include artificial intelligence, evolutionary algorithms, optimization, and robot learning.
+
+**Website:** [https://github.com/iamtsac](https://github.com/iamtsac)
+
 <!-- ## Konstantinos Tsinganos
 
 ![Konstantinos Tsinganos](images/kt.jpg){: style="width:10%"}