Papers
15
Total Citations
203
H-Index
8
About
Sipu Ruan is a robotics researcher whose work bridges motion planning, multi-robot calibration, and geometric computing. His key research areas include path planning in narrow passages, probabilistic calibration for multi-robot systems, and collision detection using convex bodies. Ruan's most impactful contribution is his 2022 paper on efficient path planning for robots with ellipsoidal components, which has garnered 48 citations and addresses computational bottlenecks in sampling-based planners like PRM and RRT. His 2018 work on the AXB = YCZ calibration problem (45 citations) provides probabilistic solutions for multi-robot coordination, while his 2018 paper on pose changes (26 citations) reinterprets rigid-body motions through group theory, offering a fresh perspective on classical transformations. Ruan's 2023 paper on marching-primitives for shape abstraction from signed distance functions (11 citations) advances computer vision by enabling compact representations for tasks like physics simulation and robotic manipulation. His 2024 PRIMP method (10 citations) introduces probabilistically-informed motion primitives for learning from demonstration, enhancing affordance learning in robot manipulators. With over 180 total citations, Ruan's work is notable for its mathematical rigor and practical impact on robot autonomy and geometric reasoning.
Research Focus
Key Achievements
Top Papers
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- 3Pose Changes From a Different Point of View26 citations · 2018
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- 5Marching-Primitives: Shape Abstraction from Signed Distance Function11 citations · 2023
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- 8Pose Changes From a Different Point of View8 citations · 2017
- 9Quantizing Euclidean Motions via Double-Coset Decomposition8 citations · 2019
- 10