Yimin Han
Papers
2
Total Citations
6
H-Index
2
About
Yimin Han is a robotics researcher whose work focuses on advancing autonomous locomotion for quadrupedal robots, particularly in complex and unstructured environments. His key contributions lie at the intersection of reinforcement learning, computer vision, and safe navigation. In his highly cited 2025 paper, "Learning Aggressive Animal Locomotion Skills for Quadrupedal Robots Solely from Monocular Videos," Han tackles the challenge of scaling agile movement without expensive motion capture systems. By learning directly from 2D video, his method overcomes the limitations of handcrafted reward design and costly 3D references, enabling robots to replicate dynamic animal gaits. This work has already garnered significant attention, with 3 citations in its first year. In parallel, his paper "Learning Autonomous and Safe Quadruped Traversal of Complex Terrains Using Multi-Layer Elevation Maps" addresses robust navigation across cluttered landscapes, using multi-layer elevation maps to ensure safety and adaptability. Han’s research is notable for its practical approach to scaling robot learning, reducing dependency on specialized hardware while pushing the boundaries of legged mobility. His achievements mark him as a rising innovator in autonomous robotics, with clear potential for real-world applications in search-and-rescue and exploration.
Research Focus
Key Achievements
Top Papers
- 1
- 2