基于学习的无人机控制器，能自适应极端参数差异。
A Learning-based Quadcopter Controller with Extreme Adaptation
- 融合模仿学习与强化学习，从传感器历史推断飞行器参数
- 实测可应对3.7倍质量差异、100倍桨叶常数变化
- 无需精确建模或调参，适合快速部署于复杂多变环境
本文提出一种基于学习的四旋翼低层控制器，可自适应处理质量、尺寸和执行器能力显著不同的四旋翼。方法结合模仿学习与强化学习，通过分析传感器-动作历史估计飞行器系统参数的隐表示，实现快速泛化控制，无需精确模型估计或手动调参。仿真测试表明，该控制器在未见过的四旋翼参数下仍具良好泛化能力，适应范围比训练集宽达16倍。真实场景中，控制器成功部署于质量相差3.7倍、螺旋桨常数差异超过100倍的无人机上，并能快速应对偏心负载和电机故障等扰动。结果证明该控制器在极端适应性方面具有潜力，可简化设计流程，提升无人机在不可预测环境中的自主运行可靠性。视频与代码见：https://github.com/muellerlab/xadapt_ctrl
原文摘要 · Abstract (English)
This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller's ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller in extreme adaptation to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments. The video and code are at: https://github.com/muellerlab/xadapt_ctrl
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