# Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control

arXiv: https://arxiv.org/abs/2508.03043 · HTML: https://mlsanity.com/paper/2508.03043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentation](https://mlsanity.com/paper/2306.00286/) (2023)
- [RoboFly: An insect-sized robot with simplified fabrication that is capable of flight, ground, and water surface locomotion](https://mlsanity.com/paper/2001.02320/) (2020)
- [Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC](https://mlsanity.com/paper/2209.10007/) (2022)
- [Robust Collaborative Object Transportation Using Multiple MAVs](https://mlsanity.com/paper/1711.08753/) (2017)
- [Modeling and LQR Control of Insect Sized Flapping Wing Robot](https://mlsanity.com/paper/2406.20061/) (2024)
- [TinySense: A Lighter Weight and More Power-efficient Avionics System for Flying Insect-scale Robots](https://mlsanity.com/paper/2501.03416/) (2025)
- [Hardware-in-the-Loop for Characterization of Embedded State Estimation for Flying Microrobots](https://mlsanity.com/paper/2411.06382/) (2024)

## Abstract (arXiv)

Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance. In contrast, insect-scale aerial robots are limited to tracking non-aggressive trajectories with small body acceleration. This performance gap is contributed by a combination of low robot inertia, fast dynamics, uncertainty in flapping-wing aerodynamics, and high susceptibility to environmental disturbance. Executing highly dynamic maneuvers requires the generation of aggressive flight trajectories that push against the hardware limit and a high-rate feedback controller that accounts for model and environmental uncertainty. Here, through designing a deep-learned robust tube model predictive controller, we showcase insect-like flight agility and robustness in a 750-millgram flapping-wing robot. Our model predictive controller can track aggressive flight trajectories under disturbance. To achieve a high feedback rate in a compute-constrained real-time system, we design imitation learning methods to train a two-layer, fully connected neural network, which resembles insect flight control architecture consisting of central nervous system and motor neurons. Our robot demonstrates insect-like saccade movements with lateral speed and acceleration of 197 centimeters per second and 11.7 meters per second square, representing 447% and 255% improvement over prior results. The robot can also perform saccade maneuvers under 160 centimeters per second wind disturbance and large command-to-force mapping errors. Furthermore, it performs 10 consecutive body flips in 11 seconds - the most challenging maneuver among sub-gram flyers. These results represent a milestone in achieving insect-scale flight agility and inspire future investigations on sensing and compute autonomy.