论文标题
使用仿真优化来改善二次旋转的零射击策略转移
Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors
论文作者
论文摘要
在这项工作中，我们提出了一种数据驱动的方法，以优化模拟的参数，以便可以将控制策略直接从仿真传输到现实世界四四极管。我们的基于神经网络的策略仅将板载传感器数据作为输入，并完全在嵌入式硬件上运行。在广泛的现实实验中，我们将低级脉冲宽度调制控制与高级控制结构（例如态度率和态度）进行比较，态度率和态度使用比例综合衍生的控制器与输出电动机命令。我们的实验表明，接受加固学习训练的低级控制器需要比更高级别的控制策略进行更准确的模拟。
In this work, we propose a data-driven approach to optimize the parameters of a simulation such that control policies can be directly transferred from simulation to a real-world quadrotor. Our neural network-based policies take only onboard sensor data as input and run entirely on the embedded hardware. In extensive real-world experiments, we compare low-level Pulse-Width Modulated control with higher-level control structures such as Attitude Rate and Attitude, which utilize Proportional-Integral-Derivative controllers to output motor commands. Our experiments show that low-level controllers trained with reinforcement learning require a more accurate simulation than higher-level control policies.
