Yaqiang Mo
Papers
4
Total Citations
23
H-Index
3
About
Yaqiang Mo is a robotics researcher whose work focuses on the intersection of robot learning, manipulation, and automation, with a particular emphasis on enabling robots to perform complex, multi-step tasks in unstructured environments. His key research areas include learning from demonstration, reinforcement learning, and pose estimation for industrial and domestic applications. Mo’s major contributions are centered on developing methods that allow robots to acquire and execute dexterous manipulation skills. His most cited work (10 citations) addresses the challenging problem of pose estimation for small connectors attached to cables, a critical step toward automating cable insertion tasks in electronics manufacturing. He has also advanced the field of robot learning by combining learning-from-demonstration with policy-search algorithms to teach robots multi-step motions (6 citations), and has explored the acquisition of folding behaviors for deformable objects like shirts using dual-arm robots (4 citations). Additionally, his work on the "MITATE" technique (3 citations) introduces a novel, user-friendly method for teaching tool operations through human demonstration, making robot programming accessible to non-experts. With a growing citation record and a focus on practical, real-world manipulation challenges, Mo’s research is paving the way for more adaptable and capable robotic systems in both industrial and assistive settings.
Research Focus
Key Achievements
Top Papers
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