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

arXiv: https://arxiv.org/abs/2209.11043 · HTML: https://mlsanity.com/paper/2209.11043/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors](https://mlsanity.com/paper/1903.04628/) (2019)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Robust Active Visual Perching with Quadrotors on Inclined Surfaces](https://mlsanity.com/paper/2204.02458/) (2022)
- [Optimal Inverted Landing in a Small Aerial Robot with Varied Approach Velocities and Landing Gear Designs](https://mlsanity.com/paper/2111.03539/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching](https://mlsanity.com/paper/2403.00128/) (2024)
- [From Ceilings to Walls: Universal Dynamic Perching of Small Aerial Robots on Surfaces with Variable Orientations](https://mlsanity.com/paper/2412.19765/) (2024)
- [Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2502.12355/) (2025)

## Abstract (arXiv)

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.