Papers
8
Total Citations
56
H-Index
5
About
Huitan Mao is a leading researcher in autonomous robotic manipulation, focusing on enabling robots to operate intelligently in dynamic, unstructured environments. His work spans real-time motion planning, task-constrained manipulation, and object estimation through touch. Mao’s most cited paper (17 citations) introduces a conflict-resolution framework for real-time adaptive motion planning (RAMP), allowing manipulators to handle unforeseen obstacles while maintaining task constraints—a critical advance for industrial and service robotics. He further pioneered null-space motion techniques to balance obstacle avoidance with task execution (10 citations), and developed force-forecast methods to reduce pose estimation uncertainty under complex contacts (7 citations). Mao’s innovative use of continuum manipulators for progressive object modeling and shape estimation (6 and 4 citations) demonstrates his commitment to integrating perception and manipulation. Notably, his reinforcement learning algorithm for estimating centers of mass of arbitrary objects (5 citations) and sim-to-real transferable touch-based classification (4 citations) highlight his contributions to robot learning and adaptability. With a total of over 56 citations across his key works, Mao’s research is foundational for advancing autonomous manipulation in unpredictable real-world settings.
Research Focus
Key Achievements
Top Papers
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- 5Learning to Estimate Centers of Mass of Arbitrary Objects5 citations · 2019
- 6Object Shape Estimation Through Touch-Based Continuum Manipulation4 citations · 2019
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