Robust adaptive control methods are essential for maintaining quadcopter performance under external disturbances and model uncertainties. However, fragmented evaluations across tasks, simulators, and implementations hinder systematic comparison of these methods. This paper introduces an easy-to-deploy, modular simulation testbed for quadcopter control, built on RotorPy, that enables evaluation under a wide range of disturbances such as wind, payload shifts, rotor faults, and control latency. The framework includes a library of representative adaptive and non-adaptive controllers and provides task-relevant metrics to assess tracking accuracy and robustness. The unified modular environment enables reproducible evaluation across control methods and eliminates redundant reimplementation of components such as disturbance models, trajectory generators, and analysis tools. We illustrate the testbed's versatility through examples spanning multiple disturbance scenarios and trajectory types, including automated stress testing, to demonstrate its utility for systematic analysis.

@misc{zhang2025simulationevaluationsuite,
  title={A Simulation Evaluation Suite for Robust
     Adaptive Quadcopter Control},
  author={Dingqi Zhang and Ran Tao and Sheng Cheng
     and Naira Hovakimyan and Mark W. Mueller},
  year={2025},
  eprint={2510.03471},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2510.03471},
}

This paper proposes the ProxFly, a residual deep Reinforcement Learning (RL)-based controller for close proximity quadcopter flight. Specifically, we design a residual module on top of a cascaded controller (denoted as basic controller) to generate high-level control commands, which compensate for external disturbances and thrust loss caused by downwash effects from other quadcopters. First, our method takes only the ego state and controllers' commands as inputs and does not rely on any communication between quadcopters, thereby reducing the bandwidth requirement. Through domain randomization, our method relaxes the requirement for accurate system identification and fine-tuned controller parameters, allowing it to adapt to changing system models. Meanwhile, our method not only reduces the proportion of unexplainable signals from the black box in control commands but also enables the RL training to skip the time-consuming exploration from scratch via guidance from the basic controller. We validate the effectiveness of the residual module in the simulation with different proximities. Moreover, we conduct the real close proximity flight test to compare ProxFly with the basic controller and an advanced model-based controller with complex aerodynamic compensation. Finally, we show that ProxFly can be used for challenging quadcopter in-air docking, where two quadcopters fly in extreme proximity, and strong airflow significantly disrupts flight. However, our method can stabilize the quadcopter in this case and accomplish docking.

@misc{zhang2024proxflyrobustcontrolclose,
  title={ProxFly: Robust Control for Close Proximity
    Quadcopter Flight via Residual Reinforcement Learning},
  author={Ruiqi Zhang and Dingqi Zhang and Mark W. Mueller},
  year={2024},
  eprint={2409.13193},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2409.13193},
}

This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters in the real world with differences in mass, size, motors, and propellers with mass differing by 4.5 times. In addition, we show rapid adaptation to sudden and large disturbances up to one-third of the mass of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually.

@article{zhang2023learning,
  title={Learning a Single Near-hover Position Controller
    for Vastly Different Quadcopters},
  author={Zhang, Dingqi and Loquercio, Antonio and
  Wu, Xiangyu and Kumar, Ashish and
  Malik, Jitendra and Mueller, Mark W},
  journal={arXiv preprint arXiv:2209.09232},
  year={2022}
}

Ball collection has always been a laborious and time-consuming work in tennis training. We design Tennie to automate this process, so that players and coaches can dedicate their efforts entirely to skill development. Tennie is a mobile robot that can collect tennis balls autonomously. It is equipped with a roller to collect balls and a cassie platform to move freely. We have built a prototype and demonstrated its functionality. We are currently working on adding vision into the system so that the Tennie can compute optimal paths for maximum collection efficiency based on vision inputs. Our proposal has been selected for Ignite Grant in Spring 2024 and Spark Grant in Fall 2023 by the Jacobs Institute Innovation Catalysts.