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Aerial insects exhibit agile maneuvers such as sharp braking, saccades, and body flips under disturbances; in contrast, insect-scale aerial robots are limited to tracking smooth trajectories with small acceleration. To achieve similar flight capabilities, insect-scale robots require a robust and computationally efficient controller. Here, through designing a deep-learned robust tube model predictive controller, we showcase exceptional flight agility in a 750-milligram flapping-wing robot. Our neural network controller can track aggressive trajectories and run at a high rate on a compute-constrained system. The robot demonstrates saccades with a lateral speed and acceleration of 197 centimeters per second and 11.7 meters per square second, respectively, representing improvements of 447 and 255% over prior results. The robot also performs saccades under 160–centimeters per second wind disturbance and completes 10 consecutive somersaults in 11 seconds. These results represent a milestone in achieving insect-scale flight agility and inspire future investigations on sensory and compute autonomy. 

This work presents the design, fabrication, and characterization of an airflow sensor inspired by the whiskers of animals. The body of the whisker was replaced with a fin structure in order to increase the air resistance. The fin was suspended by a micro-fabricated spring system at the bottom. A permanent magnet was attached beneath the spring, and the motion of fin was captured by a readily accessible and low cost 3D magnetic sensor located below the magnet. The sensor system was modeled in terms of the dimension parameters of fin and the spring stiffness, which were optimized to improve the performance of the sensor. The system response was then characterized using a commercial wind tunnel and the results were used for sensor calibration. The sensor was integrated into a micro aerial vehicle (MAV) and demonstrated the capability of capturing the velocity of the MAV by sensing the relative airflow during flight.