Yilang Liu

I'm a Ph.D. candidate in Mechanical Engineering at Yale University, advised by Prof. Ian Abraham. My research focuses on robotics, optimal control, and reinforcement learning, with an emphasis on sample-based control, visual policy learning, and legged locomotion. Previously, I received my M.S. from Carnegie Mellon University and dual B.E./B.S. degrees from Chongqing University and the University of Cincinnati. I have also worked as a robotics intern at Dexmate Inc.

Email  /  CV  /  Scholar  /  Github

Research

I'm interested in robotics, optimal control, reinforcement learning, and vision-based policy learning. My research develops sample-based and data-driven methods for robot control, with applications in legged locomotion, dexterous manipulation, and autonomous exploration.

We formulated adaptive search as an ergodic coverage problem on time-varying domains with flow-induced dynamics, deriving a flow-adaptive MMD-based objective that preserves coverage guarantees in ambient flows and enables effective exploration in under-actuated and open-loop settings.

We investigated a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes.

We proposed a computationally efficient algorithm for visual policy learning that leverages differentiable simulation and first-order analytical policy gradients.

Developed a snake locomotion gait policy for energy-efficient control via deep reinforcement learning

Feel free to steal this website's source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page.