Retro-RL: Reinforcing Nominal Controller with Deep Reinforcement Learning for Tilting-Rotor Drones

I Made Aswin Nahrendra, Christian Tirtawardhana, Byeongho Yu, Eungchang Mason Lee, Hyun Myung

IEEE Robotics and Automation Letters (RA-L)

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저자 : I Made Aswin Nahrendra, Christian Tirtawardhana, Byeongho Yu, Eungchang Mason Lee, Hyun Myung
논문명 : Retro-RL: Reinforcing Nominal Controller with Deep Reinforcement Learning for Tilting-Rotor Drones
학술지명 : IEEE Robotics and Automation Letters (RA-L)
발간월 : October
권호사항 : 7 / 4 (p. pp9004-9011)
SCI-E 여부 : SCI-E
초록 : 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 letter 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 propose

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