Jonathan D. Chang

I am a 3rd year Ph.D. student in the Computer Science department at Cornell University, advised by Wen Sun. Before Cornell, I recieved my bachelors in Computer Science and Applied Mathematics from Brown University in 2018, working with Stefanie Tellex and George Konidaris in the Humans to Robots (H2R) Laboratory.

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I am interested in machine learning, specifically reinforcement learning and representation learning, and its intersection with robotics. I am particularly interested in studying how to leverage expert demonstrations and learned feature representations for scalable reinforcement learning for robotic manipulation.

Leveraging offline data with only partial coverage, MILO mitigates covariate shift in imitation learning.

We show that model-based imitation learning from observations (IfO) with strategic exploration can near-optimally solve IfO both in theory and in practice.

We introduce an end-to-end method for targetable visuomotor skills as a goal-parameterized neual network policy, resulting in successfully learning a mapping from target pixel coordinates to a robot policy.

Reviewer: NeurIPS 2021

Website template is from Jon Barron's website.