Abstract
Flight control for autonomous micro aerial vehicles (MAVs) is evolving from steady flight near equilibrium points toward more aggressive aerobatic maneuvers, such as flips, rolls, and Power Loop. Although reinforcement learning (RL) has shown great potential in these tasks, conventional RL methods often suffer from low data efficiency and limited generalization. This challenge becomes more pronounced in multi-task scenarios where a single policy is required to master multiple maneuvers. In this paper, we propose a novel end-to-end multi-task reinforcement learning framework, called GEAR (Geometric Equivariant Aerobatics Reinforcement), which fully exploits the inherent SO(2) rotational symmetry in MAV dynamics and explicitly incorporates this property into the policy network architecture. By integrating an equivariant actor network, FiLM-based task modulation, and a multi-head critic, GEAR achieves both efficiency and flexibility in learning diverse aerobatic maneuvers, enabling a data-efficient, robust, and unified framework for aerobatic control. GEAR attains a 98.85\% success rate across various aerobatic tasks, significantly outperforming baseline methods. In real-world experiments, GEAR demonstrates stable execution of multiple maneuvers and the capability to combine basic motion primitives to complete complex aerobatics.
Abstract (translated)
自主微型飞行器（MAV）的飞行控制正从平衡点附近的稳定飞行转向更激进的特技动作，如翻滚、筋斗和动力环路。尽管强化学习（RL）在这些任务中表现出巨大潜力，但传统RL方法通常面临数据效率低且泛化能力有限的问题。这种挑战在需要掌握多种机动动作的多任务场景中尤为突出。本文提出了一种新的端到端多任务强化学习框架GEAR（Geometric Equivariant Aerobatics Reinforcement），该框架充分利用了MAV动力学中的SO(2)旋转对称性，并将这一属性明确地融入到了策略网络架构中。通过整合等变演员网络、FiLM基元的任务调节以及多头评论家，GEAR在学习多样化的特技机动时实现了高效性和灵活性，从而提供了一个数据效率高、鲁棒性强且统一的特技控制框架。GEAR在各种特技任务中的成功率为98.85%，显著优于基准方法。在真实世界实验中，GEAR展示出执行多种动作的稳定性，并能将基本运动原语组合以完成复杂的特技飞行。
URL
https://arxiv.org/abs/2602.10997
