I am Pei-An Hsieh, a second-year Robotics master’s student at the GRASP Lab. My research focuses on developing trustworthy, data-driven control strategies for robots. My previous work explored learning-based MPC, uncertainty quantification, and adaptive control. It has been an exciting journey, and I aim to further leverage the accurate reasoning of neural networks to achieve precise robot control while ensuring performance and safety guarantees. My dream is to enable robots to safely assist humans in everyday life. Outside of research, I enjoy playing the violin, practicing calligraphy, and playing soccer.
Selected Publications
ICRA 2025
Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control
Kong Yao Chee*, Pei-An Hsieh*, George J. Pappas, and 1 more author
Submitted to IEEE International Conference on Robotics and Automation (ICRA) May 2025
Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine learning tools can potentially be used to derive models that capture these effects, these data-driven approaches can be sample inefficient and the resulting models often do not generalize as well as their first-principles counterparts. In this work, we propose a framework that combines the benefits of first-principles modeling and data-driven approaches to construct an accurate and sample efficient representation of the complex aerodynamic effects resulting from quadrotors flying in formation. The data-driven component within our model is lightweight, making it amenable for optimization-based control design. Through simulations and physical experiments, we show that incorporating the model into a novel learning-based nonlinear model predictive control (MPC) framework results in substantial performance improvements in terms of trajectory tracking and disturbance rejection. In particular, our framework significantly outperforms nominal MPC in physical experiments, achieving a 40.1% improvement in the average trajectory tracking errors and a 57.5% reduction in the maximum vertical separation errors. Our framework also achieves exceptional sample efficiency, using only a total of 46 seconds of flight data for training across both simulations and physical experiments. Furthermore, with our proposed framework, the quadrotors achieve an exceptionally tight formation, flying with an average separation of less than 1.5 body lengths throughout the flight.
@article{KNODEDW,title={Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control},author={Chee*, Kong Yao and Hsieh*, Pei-An and Pappas, George J. and Hsieh, M. Ani},journal={Submitted to IEEE International Conference on Robotics and Automation (ICRA)},year={2025},month=may,publisher={Submitted to IEEE International Conference on Robotics and Automation (ICRA)},}
IROS 2022
Design and Evaluation of the infant Cardiac Robotic Surgical System (iCROSS)
Po-Chih Chen, Pei-An Hsieh, Jing-Yuan Huang, and 2 more authors
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Oct 2022
In this study, the infant Cardiac Robotic Surgical System (iCROSS) is developed to assist a surgeon in performing the patent ductus arteriosus (PDA) closure and other infant cardiac surgeries. The iCROSS is a dual-arm robot allowing two surgical instruments to collaborate in a narrow space while keeping a sufficiently large workspace. Compared with the existing surgical robotic systems, the iCROSS meets the specific requirements of infant cardiac surgeries. Its feasibility has been validated through several teleoperated tasks performed in the experiment. In particular, the iCROSS is able to perform surgical ligation successfully within one minute.
@article{iCROSS,title={Design and Evaluation of the infant Cardiac Robotic Surgical System (iCROSS)},author={Chen, Po-Chih and Hsieh, Pei-An and Huang, Jing-Yuan and Huang, Shu-Chien and Chen, Cheng-Wei},journal={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},year={2022},month=oct,publisher={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},}
