Publications
2026
- IROS 2026Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation FlightPei-An Hsieh*, Fengjun Yang*, Nikolai Matni, and 1 more authorIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Sep 2026
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)}, } - arxivNeural Navigation Functions for Zero-Shot Generalizable Motion PlanningBenjamin D. Shaffer, Pei-An Hsieh, Brooks Kinch, and 2 more authorsarxiv Jun 2026
We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment geometries. Neural-NF places data-driven adaptation within a structured elliptic planner, where the navigation objective is learned while planner structure is preserved by construction. Specifically, intrinsic Laplacian-derived features are mapped to local PDE coefficients, and solving the resulting boundary value problem produces a globally consistent value function on each target domain. For every admissible learned model, the resulting policy is collision-free, provides monotonic descent and a global minimum at the goal by construction. This admits a linearly-solvable optimal-control interpretation for any parameter setting. Empirically, Neural-NF achieves strong zero-shot transfer across diverse geometries and outperforms learned planners that directly predict the value function by up to a 5 times improvement.
@article{NNavigation, title = {Neural Navigation Functions for Zero-Shot Generalizable Motion Planning}, author = {Shaffer, Benjamin D. and Hsieh, Pei-An and Kinch, Brooks and Trask, Nathaniel and Hsieh, M. Ani}, year = {2026}, month = jun, }
2025
- ISER 2025Online Adaptation for Flying Quadrotors in Tight FormationsPei-An Hsieh, Kong Yao Chee, and M. Ani HsiehInternational 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 2025Flying Quadrotors in Tight Formations using Learning-based Model Predictive ControlKong Yao Chee*, Pei-An Hsieh*, George J. Pappas, and 1 more authorIEEE 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)}, }
2022
- IROS 2022Design and Evaluation of the infant Cardiac Robotic Surgical System (iCROSS)Po-Chih Chen, Pei-An Hsieh, Jing-Yuan Huang, and 2 more authorsIEEE/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)}, }