Data-Enabled Learning and Control for Robotics

Advances in data-enabled learning and control are unlocking new possibilities for robotics, allowing systems to operate with greater flexibility, efficiency, and reliability. By leveraging data to enhance decision-making and adaptability, these approaches enable robots to perform complex tasks in dynamic and uncertain environments. At the same time, ensuring stability, agility, safety, and generalization remains a key challenge, requiring innovative solutions that integrate learning and control effectively. This workshop will explore recent developments and key challenges in integrating learning techniques with control strategies to enhance robotic performance and adaptability. Through discussions on theory, algorithms, and real-world applications, the workshop aims to foster collaboration and accelerate progress toward the next generation of high-performance robotic systems.

This workshop aims to bring together researchers and practitioners to explore recent advancements, challenges, and future directions in data-enabled learning and control for robotics. The objectives are:

• To discuss the latest theoretical and practical developments in learning and control for robotics

• To foster interdisciplinary collaboration between experts in control, machine learning, and robotics

• To identify key challenges and open research questions

 If you have any questions about this workshop, please contact:

Xiaocong Li (xiaocongli@eitech.edu.cn)