Dingqi(Daisy) Zhang

I am a graduate student at UC Berkeley advised by Prof. Mark W. Mueller and Prof. Jitendra Malik. I am broadly interested in Robotics, with a focus on extreme adaptative control for aerial systems in challenging environment with real-world validation. I completed my undergraduate study at Cornell University, with a double major in Computer Science and Mechanical Engineering.

Email  /  Resume  /  Google Scholar

profile photo

Learning a Single Near-hover Position Controller for Vastly Different Quadcopters
Dingqi Zhang, Antonio Loquercio, Xiangyu Wu, Ashish Kumar,
Jitendra Malik, Mark W. Mueller
ICRA 2023

webpage | abstract | bibtex | arXiv | video

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.

  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},


Tennibot: An Antonomous Tennis Ball Collector
Dingqi Zhang*, Jerry Tang*
(SP24 Ignite Grant, 3% | FA23 Spark Grant, 9%)

Ball collection has always been a laborous and time-consuming work in tennis training. We design Tennibot to automate this process, so that players and coaches can dedicate their efforts entirely to skill development. Tennibot 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 Tennibot 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 Institude Innovation Catalysts.


In my spare time, I play tennis and climb. I'm also raising a cat!

Website adapted from Jon Barron and Ashish Kumar