A Learning-based Quadcopter Controller with Extreme Adaptation

2024-09-19

IEEE Transactions on robotics

AI Summary

Conventional low-level controllers for quadcopters require precise dynamical modeling and extensive parameter tuning, limiting generalization across platforms with substantial differences in mass, size, and actuator capabilities. Method: We propose a model-free, parameter-free learning-based low-level controller that jointly leverages imitation learning and deep reinforcement learning. It implicitly identifies system parameters online from sensor-action histories, enabling real-time adaptive control without explicit system identification. Contribution/Results: We introduce the first end-to-end latent-state system identification framework, achieving unprecedented dynamical generalization: in simulation, it adapts to unseen parameter combinations spanning 16× the training range; on physical hardware, it robustly handles 3.7× mass variation and >100× differences in propeller constants, while tolerating severe disturbances including payload asymmetry and single-motor failure. The controller has been successfully deployed on real drones, significantly enhancing the universality and engineering practicality of low-level flight control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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

Problem

• Adaptive control for quadcopters with varying mass, size, and actuator capabilities
• Eliminates need for precise model estimation or manual tuning
• Enables rapid adaptation to disturbances and diverse dynamics

Innovation

• Combines imitation and reinforcement learning
• Estimates latent system parameters dynamically
• Adapts to extreme mass and actuator variations