Learning step-level dynamic soaring in shear flow

TL;DR

This paper demonstrates that dynamic soaring in unsteady shear flows can be achieved through local, step-level control using deep reinforcement learning, without explicit cycle-level planning, and generalizes across diverse conditions.

Contribution

It introduces a feedback-based control approach for dynamic soaring that relies solely on local sensing, challenging the traditional cycle-level maneuver paradigm.

Findings

Policies achieve robust omnidirectional navigation in various shear conditions.

The control law coordinates turning and vertical motion for energy extraction.

The learned behavior aligns with biological flight features and optimal control solutions.

Abstract

Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes stable flow conditions. In realistic unsteady environments, however, such assumptions are often violated, raising the question of whether explicit cycle-level planning is necessary. Here, we show that dynamic soaring can emerge from step-level, state-feedback control using only local sensing, without explicit trajectory planning. Using deep reinforcement learning as a tool, we obtain policies that achieve robust omnidirectional navigation across diverse shear-flow conditions. The learned behavior organizes into a structured control law that coordinates turning and vertical motion, giving rise to a two-phase strategy governed by a trade-off between energy extraction and directional progress. The resulting policy generalizes across varying conditions and reproduces key features observed in biological flight and optimal-control solutions. These findings identify a feedback-based control structure underlying dynamic soaring, demonstrating that efficient energy-harvesting flight can emerge from local interactions with the flow without explicit planning, and providing insights for biological flight and autonomous systems in complex, flow-coupled environments.