Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Drones
Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many studies for robot control to accomplish complex tasks. Unfortunately, deep RL algorithms might not be suitable for being de...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
07.07.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Studies that broaden drone applications into complex tasks require a stable
control framework. Recently, deep reinforcement learning (RL) algorithms have
been exploited in many studies for robot control to accomplish complex tasks.
Unfortunately, deep RL algorithms might not be suitable for being deployed
directly into a real-world robot platform due to the difficulty in interpreting
the learned policy and lack of stability guarantee, especially for a complex
task such as a wall-climbing drone. This paper proposes a novel hybrid
architecture that reinforces a nominal controller with a robust policy learned
using a model-free deep RL algorithm. The proposed architecture employs an
uncertainty-aware control mixer to preserve guaranteed stability of a nominal
controller while using the extended robust performance of the learned policy.
The policy is trained in a simulated environment with thousands of domain
randomizations to achieve robust performance over diverse uncertainties. The
performance of the proposed method was verified through real-world experiments
and then compared with a conventional controller and the state-of-the-art
learning-based controller trained with a vanilla deep RL algorithm. |
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DOI: | 10.48550/arxiv.2207.03124 |