Yunlong Song - 宋运龙
I am currently a Research Scientist at Skild AI, developing robot learning algorithms for general-purpose robot manipulation. I completed my PhD under the supervision of Prof. Davide Scaramuzza at the Robotics and Perception Group, which is part of the Department of Neuroinformatics, a joint institute of University of Zurich and ETH Zurich. During my PhD, I also conducted research at MIT, working with Prof. Sangbae Kim on quadruped locomotion. Additionally, I had the opportunity to collaborate with SONY AI on building super-human game agent for Gran Turismo and with Vladlen Koltun on autonomous drone racing. Prior to my PhD, I earned my master's degree from TU Darmstadt, where I was supervised by Prof. Jan Peters.
PhD DefenseResearch Interests
My research focuses on developing intelligent robotic systems capable of learning, adapting, and interacting in complex real-world environments. I have explored various topics in robotics and machine learning, including optimal control, reinforcement learning, differentiable simulation, representation learning, and vision-based control. Beyond theoretical understanding, I have hands-on experience with a range of robotic platforms, including the Furuta Pendulum, Barrett WAM Arm, high-performance FPV drone, MIT Mini Cheetah, and robotic manipulators. My past research has primarily focused on developing robust skill-level policies for diverse robotic tasks. Currently, I am particularly interested in integrating large vision-language-action models with low-level robotic skills to enable autonomous systems to perform complex, real-world tasks with greater efficiency and adaptability. My current research interests include:
- Real-world Reinforcement Learning
- Reinforcement Learning for Mobile/Whole-body Manipulation
- Direct Policy Optimization for Pixel-to-Action Control
- Post-training of Robotics Foundation Models
- Multimodal Policy Learning