论文标题

学习一个近距离四肢架的单个悬而未决的位置控制器

Learning a Single Near-hover Position Controller for Vastly Different Quadcopters

论文作者

Zhang, Dingqi, Loquercio, Antonio, Wu, Xiangyu, Kumar, Ashish, Malik, Jitendra, Mueller, Mark W.

论文摘要

本文提出了针对四轮驱动器的自适应近距离位置控制器,可以将其部署到具有截然不同的质量,尺寸和运动常数的四肢旋转器,并且还显示出对运行时过程中未知干扰的快速适应。核心算法的想法是学习一个单一的策略,该策略不仅可以在测试时间在线适应无人机的干扰,而且还可以在同一框架中适用于机器人动力学和硬件。我们通过训练神经网络来估计机器人和环境参数的潜在表示,该网络用于调节控制器的行为,也表示为神经网络。我们专门训练两个网络进行模拟,目的是将四轮驱动器飞往目标位置并避免撞车到地面。我们直接在模拟中部署了相同的控制器,而没有对现实世界中两个四肢驱动器进行任何修改,其质量,尺寸,电动机和螺旋桨的差异差异为4.5倍。此外,我们对突然和大型干扰的迅速适应,最多是四分之一的四分之一的四分之一。我们在模拟和物理世界中进行了广泛的评估,在该评估中,我们的表现优于最先进的基于学习的自适应控制器和专门针对每个平台的传统PID控制器。视频结果可以在https://youtu.be/u-c-lbtfvoa上找到。

This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters in the real world with differences in mass, size, motors, and propellers with mass differing by 4.5 times. In addition, we show rapid adaptation to sudden and large disturbances up to one-third of the mass of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://youtu.be/U-c-LbTfvoA.

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