Jonathan D. Chang

I am a 4th 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.

Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan D. Chang*, Kaiwen Wang*, Nathan Kallus, Wen Sun
ICML 2022 (Long Talk), 2022
code / arXiv
profile photo Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang*, Masatoshi Uehara*, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
NeurIPS 2021, 2021
code / arXiv

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

profile photo MobILE: Model-Based Imitation Learning from Observation Alone
Rahul Kidambi, Jonathan D. Chang, Wen Sun
NeurIPS 2021, 2021

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

profile photo Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan D. Chang*, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex

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, ICML 2022, ICLR 2022, NeurIPS 2022

Website template is from Jon Barron's website.