Yongbo Su

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About

Yongbo Su is a leading researcher in humanoid robotics, with a primary focus on perceptive locomotion and whole-body control. His most notable contribution is the development of learning-based frameworks that enable humanoid robots to traverse challenging, unstructured terrain—a critical step beyond traditional proprioception-only controllers. In his seminal 2025 work, "Learning Perceptive Humanoid Locomotion over Challenging Terrain," Su demonstrates how integrating visual and tactile perception with reinforcement learning allows robots to adapt in real-time to obstacles, slopes, and deformable surfaces. This approach addresses a fundamental limitation in existing systems, which often fail when visual cues are absent or when terrain unpredictability exceeds model-based predictions. While his citation count is still growing, the paper has already been recognized as a foundational reference in the emerging field of perceptive locomotion. Su’s work bridges the gap between simulation and real-world deployment, offering a scalable path toward humanoid robots that can operate safely in human environments. His research is particularly influential for students and engineers working on legged robotics, as it provides both theoretical insights and practical algorithms for robust, adaptive locomotion.