Papers
2
Total Citations
6
H-Index
2
About
Haochen Zheng is a rising researcher at the intersection of robotics, machine learning, and medical/surgical automation. His work focuses on enabling robots to learn complex, contact-rich manipulation skills from human demonstrations—particularly for high-stakes applications like soft tissue puncture and heterogeneous component machining. Zheng’s major contributions include a novel framework for learning target-directed skill and variable impedance control from interactive demonstrations, specifically designed to simplify and improve the precision of robot-assisted percutaneous puncture surgery. This work, published in 2024, has already garnered 4 citations, signaling early impact in the surgical robotics community. He has further advanced the field by developing adaptive control strategies that guarantee stability while learning from demonstrations, a critical requirement for real-world robotic tasks. His 2025 paper on learning stability-guaranteed skill for heterogeneous component robotic machining (2 citations) extends these principles to manufacturing contexts. By bridging the gap between human dexterity and robotic precision, Zheng is laying foundational work for safer, more capable robotic assistants in both medicine and industry.
Research Focus
Key Achievements
Top Papers
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- 2