Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers

TL;DR

This paper develops a deep reinforcement learning-based control policy for small aerial robots to perform inverted landings reliably using onboard sensing, validated through simulation and real-world experiments.

Contribution

It introduces a novel RL-based control framework for inverted landing, including sim-to-real transfer via domain randomization, enhancing robustness and generalization.

Findings

Optimized control policy for inverted landing from diverse approach conditions.

Successful sim-to-real transfer validated through experimental trials.

Identification of key factors improving landing robustness.

Abstract

Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. In spite of this, this feat is routinely performed by biological fliers such as bats, flies, and bees. Our previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing starting from any arbitrary approach condition. This optimized control policy provides a computationally-efficient mapping from the system's observational space to its motor command action space, including both triggering and control of rotational maneuvers. This was done by training the system over a large range of approach flight velocities that varied with magnitude and direction. Next, we performed a sim-to-real transfer and experimental validation of the learned policy via domain randomization, by varying the robot's inertial parameters in the simulation. Through experimental trials, we identified several dominant factors which greatly improved landing robustness and the primary mechanisms that determined inverted landing success. We expect the learning framework developed in this study can be generalized to solve more challenging tasks, such as utilizing noisy onboard sensory data, landing on surfaces of various orientations, or landing on dynamically-moving surfaces.