I am Pei-An Hsieh, a first-year Ph.D. student advised by Prof. M. Ani Hsieh at the GRASP Lab. My research focuses on developing trustworthy, data-driven control strategies for multi-robot systems. My previous work has explored learning-based model predictive control (MPC), uncertainty quantification, and adaptive control. It has been an exciting journey, and I aim to further harness the reasoning capabilities of neural networks to achieve precise robot control with strong performance and safety guarantees. My long-term goal is to enable robots to safely and efficiently collaborate in real-world environments. Outside of research, I enjoy playing the violin, practicing calligraphy, and playing soccer.
Selected Publications
ISER 2025
Online Adaptation for Flying Quadrotors in Tight Formations
Pei-An Hsieh, Kong Yao Chee, and M. Ani Hsieh
International Symposium on Experimental Robotics (ISER) Jul 2025
The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as the team. Furthermore, these aerodynamic effects are highly nonlinear and fast-paced, making them difficult to model and predict. To overcome these challenges, we present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework that allows individual quadrotors to accurately track trajectories while adapting to time-varying aerodynamic interactions during formation flights. We evaluate L1 KNODE-DW MPC in two different three-quadrotor formations and show that it outperforms several MPC baselines. Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight. These findings show that the L1 adaptive module compensates for unmodeled disturbances most effectively when paired with an accurate dynamics model.
@article{L1KNODEDW,title={Online Adaptation for Flying Quadrotors in Tight Formations},author={Hsieh, Pei-An and Chee, Kong Yao and Hsieh, M. Ani},journal={International Symposium on Experimental Robotics (ISER)},year={2025},month=jul,publisher={International Symposium on Experimental Robotics (ISER)},}
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
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={IEEE International Conference on Robotics and Automation (ICRA)},year={2025},month=may,publisher={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)},}
