arXiv subjects

Dingqi Zhang

Publications and source records attributed to Dingqi Zhang.

5 recordsLinked to original sources

arXiv2026

Interpreting Control Latents for System Identification via Conditional Flow Matching

Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by $23\%$ and heading RMSE by $45\%$ relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.

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arXiv2025

A Simulation Evaluation Suite for Robust Adaptive Quadcopter Control

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. Code is available at https://github.com/Dz298/AdaptiveQuadBench.

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arXiv2024

ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning

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 mid-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. The resources are available at https://github.com/ruiqizhang99/ProxFly.

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arXiv2024

A Learning-based Quadcopter Controller with Extreme Adaptation

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

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arXiv2023

Learning a Single Near-hover Position Controller for Vastly Different Quadcopters

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. Video results can be found at https://youtu.be/U-c-LbTfvoA.

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