Papers
22
Total Citations
425
H-Index
9
About
Jingpei Lu is a robotics researcher whose work spans surgical robot autonomy, robot perception, pose estimation, and deformable object manipulation. With over 380 cumulative citations, Lu has established a strong presence at the intersection of computer vision, deep learning, and robotic systems. A cornerstone of Lu's contributions is the SuPer Deep framework — developed across multiple iterations — which leverages deep learning for surgical tool tracking and deformable tissue reconstruction, replacing labor-intensive hand-engineered features and advancing the viability of autonomous robotic surgery. Building on this foundation, Lu extended the framework with semantic awareness in Semantic-SuPer, enabling richer endoscopic scene understanding. His work on real-to-sim registration of soft tissue and differentiable position-based dynamics for rope-like object manipulation addresses fundamental challenges in modeling and controlling deformable materials in surgical contexts. Lu has also made significant contributions to markerless pose estimation via sim-to-real transfer and differentiable rendering, enabling more practical, marker-free robot calibration. His involvement in the large-scale DROID robot manipulation dataset (108 citations) reflects his engagement with community-wide efforts to advance generalist robot learning, underscoring his broad impact across both surgical and general-purpose robotics research.
Research Focus
Key Achievements
Top Papers
- 1DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset108 citations · 2024
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