PEI-AN HSIEH

Selected Publications

1. IROS 2026

   Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight

   Pei-An Hsieh, Fengjun Yang, Nikolai Matni, and 1 more author

   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Sep 2026

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   Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To compensate for these effects, we propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat. We leverage this preserved flatness to design a computationally efficient feedback linearization controller that is easily tunable with linear control techniques and cancels aerodynamic disturbances via feedforward compensation. Hardware experiments demonstrate our framework reduces average tracking errors by 31% compared to nominal baselines. Crucially, our lightweight approach matches the tracking performance of state-of-the-art nonlinear model predictive control (NMPC) while requiring an order of magnitude less computation. We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks.

   @article{Learned_FBL,
     title = {Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight},
     author = {Hsieh, Pei-An and Yang, Fengjun and Matni, Nikolai and Hsieh, M. Ani},
     journal = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
     year = {2026},
     month = sep,
     publisher = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
   }

2. 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

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   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)},
   }

3. 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

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   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)},
   }