mlsanityData to 14 Oct 2026

RL-based Control of UAS Subject to Significant Disturbance

arXiv 2504.08114 ↗First posted 10 Apr 2025cs.RO cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 14 references that are in the map. First posted on arXiv 10 Apr 2025.

Builds on

8 of 14 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control10 Oct

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Learning High-Speed Flight in the Wildcited by 128
  3. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  4. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
  5. 2025Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robotscited by 15
  6. 2024Learning Speed Adaptation for Flight in Cluttercited by 14
  7. 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learningcited by 8

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Not cited yet.

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  1. 2025Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs
  2. 2024Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
  3. 2025Reinforcement Learning for Autonomous Point-to-Point UAV Navigation
  4. 2026Safe Learning-Based Adaptive Augmentation Control for Fixed-Wing UAV under Uncertainty ↗
  5. 2026A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions
  6. 2026Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
  7. 2026Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
  8. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking