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.

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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 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

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