Yu-Shun Hsiao
Papers
9
Total Citations
84
H-Index
5
About
Yu-Shun Hsiao is a leading researcher at the intersection of robotics, autonomous systems, and dependable computing. His work focuses on ensuring the safety, resilience, and performance of learning-based navigation and micro aerial vehicles (UAVs). Hsiao’s major contributions include developing end-to-end fault analysis frameworks—such as MAVFI and ROSFI—that systematically assess the impact of silent data corruption on mission-critical metrics like flight time and success rate. These works, with over 20 and 17 citations respectively, have been instrumental in advancing fault tolerance for autonomous drones. He also created RobotPerf, a vendor-agnostic benchmarking suite for robotics computing performance, and OMU, a probabilistic 3D occupancy mapping accelerator for real-time OctoMap at the edge. His research on variable-precision tensors for robot motion planning and caching strategies for 3D mapping further demonstrates his commitment to optimizing both accuracy and efficiency. With a growing citation record and a portfolio of high-impact tools and methodologies, Hsiao is shaping the future of resilient, high-performance autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems22 citations · 2021
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- 7VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning3 citations · 2023
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