Insects have long been recognized for their ability to navigate and return home using visual cues from their nest's environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method that directly learns the home vector direction from visual percepts during a learning flight in the vicinity of the nest. After learning, the robot will travel away from the nest, come back by means of odometry, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. Using a compact convolutional neural network, we demonstrate successful learning in both simulated and real forest environments, as well as successful homing control of a simulated quadrotor. The average errors of the inferred home vectors in general stay well below the 90{\deg} required for successful homing, and below 24{\deg} if all images contain sufficient texture and illumination. Moreover, we show that the trajectory followed during the initial learning flight has a pronounced impact on the network's performance. A higher density of sample points in proximity to the nest results in a more consistent return. Code and data are available at https://mavlab.tudelft.nl/learning_to_home .

翻译：长期以来，昆虫因其利用巢穴环境视觉线索进行导航并返回巢穴的能力而备受关注。然而，这种卓越的归巢技能背后的确切机制仍是研究中的待解之谜。受蜜蜂和黄蜂的学习飞行行为启发，我们提出了一种机器人导航方法，该方法在学习飞行期间直接根据巢穴周围的视觉感知学习归巢向量方向。学习完成后，机器人将远离巢穴，通过里程计返回，并通过从当前视野中推断归巢向量方向来消除累积的漂移。利用紧凑型卷积神经网络，我们在模拟和真实森林环境中均成功实现了学习，并成功控制模拟四旋翼飞行器完成归巢任务。推断出的归巢向量的平均误差通常远低于成功归巢所需的90度，且当所有图像均包含足够纹理和光照时，平均误差低于24度。此外，我们表明初始学习飞行过程中的轨迹对网络性能有显著影响。靠近巢穴的采样点密度越高，返回的一致性越强。代码和数据可在 https://mavlab.tudelft.nl/learning_to_home 获取。