Fatemeh Zargarbashi

Robotics, Character animation, Reinforcement learning

prof_pic.jpg

PhD Candidate,

ETH Zurich,

Disney research | Studios

Hi! I’m Fatemeh.

I am a Ph.D. candidate in Computer Science jointly at ETH Zurich (Computational Robotics Lab) and Disney research Studios. My research lies in bridging the gap between robotics and character animation by investigating how to develop natural motions on legged robots and how to control physics-based animated characters. I am also interested in developing reinforcement learning algorithms for character control, legged locomotion, and motion generation.

I received my M.Sc. in Mechanical Engineering, Control from Sharif University of Technology, Tehran, Iran. During my master thesis I worked on the development and control of an over-actuated quadcopter for agile maneuvers.

In my free time, I like cycling, swimming and reading poetry. I am also an amateur astronomer and I like to go stargazing :telescope: whenever I have the chance. Recently, I have started exploring night sky photography :camera:!

news

Jul 05, 2025 We hosted a booth in the annual ETH RobotX innovation day, with a couple of demos of our research and robots! see post on linkedin
Sep 05, 2024 I’m thrilled to share that our paper, “RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards”, has been accepted at CoRL 2024! 🎉 [arxiv], [youtube]

latest posts

selected publications

  1. SteerableAnimal.png
    Learning Steerable Imitation Controllers from Unstructured Animal Motions
    Dongho Kang, Jin Cheng, Fatemeh Zargarbashi, and 3 more authors
    arXiv preprint arXiv:2507.00677, 2025
  2. RobotKeyframing.png
    RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
    Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, and 2 more authors
    8th Conference on Robot Learning (CoRL), 2024
  3. MetaLoco.png
    MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
    Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng, and 3 more authors
    arXiv preprint arXiv:2407.17502, 2024
  4. DeepCompliantControl.jpg
    Deep Compliant Control for Legged Robots
    Adrian Hartmann, Dongho Kang, Fatemeh Zargarbashi, and 2 more authors
    In 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024
  5. RL_MBOC.png
    Rl+ model-based control: Using on-demand optimal control to learn versatile legged locomotion
    Dongho Kang, Jin Cheng, Miguel Zamora, and 2 more authors
    IEEE Robotics and Automation Letters, 2023