Papers
9
Total Citations
229
H-Index
5
About
Zhengping Che is a researcher whose work spans two remarkably distinct yet forward-looking domains: surgical robotics and embodied artificial intelligence. Early in his career, Che made a significant impact in clinical AI, co-authoring a highly influential 2018 study that applied machine learning to automated performance metrics for evaluating robot-assisted radical prostatectomy — work that has since garnered 181 citations and helped establish a new paradigm for data-driven surgical assessment and outcome prediction. More recently, Che has redirected his focus toward the cutting edge of robotic manipulation and embodied AI, contributing to a growing body of work that explores how robots can understand and act upon natural language instructions. His notable contributions include the RoboMIND benchmark — a large-scale dataset of over 107,000 demonstration trajectories designed to advance multi-embodiment robot learning — as well as research on language-conditioned manipulation inspired by dual-process cognitive theory and object-centric instruction augmentation. He has also contributed surveys synthesizing the role of foundation models in robotics. Collectively, Che's research reflects a sustained commitment to bridging intelligent perception, reasoning, and physical action, positioning him as an emerging voice in the embodied AI community.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Language-Conditioned Robotic Manipulation with Fast and Slow Thinking10 citations · 2024
- 4Object-Centric Instruction Augmentation for Robotic Manipulation8 citations · 2024
- 5
- 6A Survey on Robotics with Foundation Models: toward Embodied AI4 citations · 2024
- 7
- 8Diffusion Trajectory-Guided Policy for Long-Horizon Robot Manipulation2 citations · 2025
- 9