Gaoyue (Kathy) Zhou

I am a second-year MS in Robotics student at CMU Robotics Institute advised by Abhinav Gupta.

Previously, I did my undergraduate in Computer Science and Applied Mathematics at UC Berkeley. During my time at Berkeley, I had the pleasure to work with Prof. Sergey Levine and Prof. John DeNero in the Berkeley Artificial Intelligence Research (BAIR) Lab.

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I am broadly interested in Robotics, Reinforcement Learning, and NLP. My motivation is to build agents with cognitive capabilities that can learn and infer like humans.

project image Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou*, Victoria Dean*, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta
In submission to ICRA 2023
Accepted to Oral at NeurIPS WBRC 2022
OpenReview | video | project page

We introduce a new benchmark for tabletop manipulation: Train Offline, Test Online (TOTO) and showed performance of state-of-the-art models on two tasks.

project image Real World Offline Reinforcement Learning with Realistic Data Source
Gaoyue Zhou*, Liyiming Ke*, Siddhartha Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar
In submission to ICRA 2023
Accepted to 3 NeurIPS 2022 workshops
arXiv | video | project page

In this work, we evaluate Offline RL algorithms' performance on real robot, on in-domain tasks and transfer learning settings.

project image Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
Samee Ibraheem*, Gaoyue Zhou*, John DeNero
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022
Oral Presentation
arXiv | video | project page

In this paper, we collect and release a dataset for identifying deceptive actors through the game of Mafia, as well as train models to identify such actors and reveal features of their language.

project image Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Avi Singh*, Huihan Liu*, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine
International Conference on Learning Representations (ICLR), 2021
Oral Presentation (top 1.8% of submissions)
arXiv | video | project page

We proposed a method for pre-training a behavioral prior for reinforcement learning using data from a diverse range of tasks, and used this behavioral prior to speed up learning of new tasks.

project image Modeling Eva Hild's Sculpture "Wholly"
Advised by Prof. Carlo H. Séquin
tech report

We proposed various ways to use CAD Tools for the Procedural Generation of 2-Manifold Sculpture Geometries with high-level control.


10725A: Convex Optimization , Fall 2022

CS 189: Introduction to Machine Learning , Fall 2020

EECS 16B (Designing Information Devices and Systems II) , Fall 2019, Spring 2020, Spring 2021

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