Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise triggering and control of body flips that lead to a variety of successful landing behaviors. Building upon this knowledge, we aimed to replicate the flies' landing behaviors in small quadcopters by developing a control policy general to arbitrary ceiling-approach conditions. First, we employed reinforcement learning in simulation to optimize discrete sensory-motor pairs across a broad spectrum of ceiling-approach velocities and directions. Next, we converted the sensory-motor pairs to a two-stage control policy in a continuous augmented-optical flow space. The control policy consists of a first-stage Flip-Trigger Policy, which employs a one-class support vector machine, and a second-stage Flip-Action Policy, implemented as a feed-forward neural network. To transfer the inverted-landing policy to physical systems, we utilized domain randomization and system identification techniques for a zero-shot sim-to-real transfer. As a result, we successfully achieved a range of robust inverted-landing behaviors in small quadcopters, emulating those observed in flies.

翻译：倒立着陆是多种动物飞行者中的常见行为。然而，掌握这一技能对机器人飞行器而言是一项重大挑战，尤其是实现伴随快速身体旋转（或翻转）并克服重力的动态栖止着陆。果蝇的倒立着陆研究表明，光流感知与精确触发和控制导致多种成功着陆行为的身体翻转密切相关。基于这一知识，我们旨在通过开发一种适用于任意天花板接近条件的通用控制策略，在小四旋翼无人机上复现果蝇的着陆行为。首先，我们在仿真中采用强化学习，优化了跨越广泛天花板接近速度和方向的离散感觉-运动对。随后，我们将这些感觉-运动对转化为连续增广光流空间中的两阶段控制策略。该控制策略包括第一阶段翻转触发策略（采用一类支持向量机）和第二阶段翻转动作策略（实现为前馈神经网络）。为将倒立着陆策略迁移至物理系统，我们利用领域随机化和系统辨识技术实现了零样本仿真到现实的迁移。最终，我们成功在小四旋翼无人机上实现了多种鲁棒的倒立着陆行为，模拟了果蝇的相应表现。