A Learning-based Quadcopter Controller with Extreme Adaptation

2024/09/19 by Dingqi Zhang, Zhang, Dingqi, Antonio Loquercio +9 · 7 citations
Computer Science · Engineering · #Advanced Control Systems Design #Advanced Data Processing Techniques #FOS: Computer and information sciences #Machine Learning and ELM #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2409.12949

openalex publication_date 2024/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller's ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller in extreme adaptation to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments. The video and code are at: https://github.com/muellerlab/xadapt_ctrl

Cited by

• Agile Flight Emerges from Multi-Agent Competitive Racing
• Simultaneous learning of state-to-state minimum-time planning and control
• AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control
• A Simulation Evaluation Suite for Robust Adaptive Quadcopter Control
• Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
• RAPTOR: A Foundation Policy for Quadrotor Control
• Adaptive Lattice-based Motion Planning

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