Precise aggressive aerial maneuvers with sensorimotor policies

Precise aggressive maneuvers with lightweight onboard sensors remain a key bottleneck in fully exploiting the maneuverability of drones. Such maneuvers are critical for expanding the systems' accessible area by navigating through narrow openings in the environment. One of the most relevant problems is aggressive traversal through narrow gaps with quadrotors under constraints in the special Euclidean group of three dimensions [[Formula: see text]], which requires the quadrotors to leverage a momentary tilted attitude and the asymmetry of the airframes to navigate through gaps. Here, we achieved such maneuvers by developing sensorimotor policies directly mapping onboard vision and proprioception into low-level control commands. The policies were trained using reinforcement learning (RL) with end-to-end policy distillation in simulation. We mitigated the model-free RL's exploration challenge on the restricted solution space with an initialization strategy leveraging trajectories generated by a model-based planner. Careful sim-to-real design allowed the policy to control a quadrotor through narrow gaps with low clearances and high repeatability. For instance, the proposed method enabled a quadrotor to navigate a rectangular gap at a 5-centimeter clearance, tilted at an orientation up to 90°, without knowledge of the gap's position or orientation. Without training on dynamic gaps, the policy could reactively servo the quadrotor to traverse through a moving gap. The proposed method was validated on challenging tracks of narrow, closely placed gaps. The flexibility of the policy learning method was demonstrated by developing policies on geometrically diverse gaps without relying on manually defined traversal poses and visual features.