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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 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 emulated observational space to its motor command action space, including both triggering and control of rotational maneuvers. This was accomplished 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 reinforcement 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.

Original languageEnglish (US)
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9798350323658
StatePublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: May 29 2023Jun 2 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this