REPLAB: A Reproducible Low-Cost Arm Benchmark for Robotic Learning. - Brian H. Yang; Dinesh Jayaraman; Jesse Zhang; Sergey Levine (ICRA, 2019)

TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications. - Jesse Zhang; Jack Sullivan; Vasudev Venkatesh P. B.; Kyle Tse; Andy Yan; John Leyden; Kalyanaraman Shankari; Randy H. Katz (BuildSys, 2019)

TRECVID 2019: An evaluation campaign to benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & retrieval. - George Awad; Asad A. Butt; Keith Curtis; Yooyoung Lee; Jonathan G. Fiscus; Afzal Godil; Andrew Delgado; Jesse Zhang; Eliot Godard; Lukas L. Diduch; Alan F. Smeaton; Yvette Graham; et al. (TRECVID, 2019)

REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning. - Brian H. Yang; Jesse Zhang; Vitchyr Pong; Sergey Levine; Dinesh Jayaraman (CoRR, 2019)

Unsupervised Projection Networks for Generative Adversarial Networks. - Daiyaan Arfeen; Jesse Zhang (CoRR, 2019)

Chaining Behaviors from Data with Model-Free Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRL, 2020)

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. - Jesse Zhang; Brian Cheung; Chelsea Finn; Sergey Levine; Dinesh Jayaraman (ICML, 2020)

TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains. - George Awad; Asad A. Butt; Keith Curtis; Jonathan G. Fiscus; Afzal Godil; Yooyoung Lee; Andrew Delgado; Jesse Zhang; Eliot Godard; Baptiste Chocot; Lukas L. Diduch; Jeffrey Liu; et al. (TRECVID, 2020)

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRR, 2020)

Hierarchical Reinforcement Learning by Discovering Intrinsic Options. - Jesse Zhang; Haonan Yu; Wei Xu 017 (ICLR, 2021)

Learning to Synthesize Programs as Interpretable and Generalizable Policies. - Dweep Trivedi; Jesse Zhang; Shao-Hua Sun; Joseph J. Lim (NeurIPS, 2021)

Greenplum: A Hybrid Database for Transactional and Analytical Workloads. - Zhenghua Lyu; Huan Hubert Zhang; Gang Xiong; Gang Guo; Haozhou Wang; Jinbao Chen; Asim Praveen; Yu Yang; Xiaoming Gao; Alexandra Wang; Wen Lin 004; Ashwin Agrawal; et al. (SIGMOD Conference, 2021)

2020 Sequestered Data Evaluation for Known Activities in Extended Video: Summary and Results. - Afzal Godil; Yooyoung Lee; Jon Fiscus; Andrew Delgado; Eliot Godard; Baptiste Chocot; Lukas L. Diduch; Jim Golden; Jesse Zhang (WACV (Workshops), 2021)

Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance. - Jesse Zhang; Jiahui Zhang; Karl Pertsch; Ziyi Liu; Xiang Ren; Minsuk Chang; Shao-Hua Sun; Joseph J. Lim (CoRL, 2023)

RoboCLIP: One Demonstration is Enough to Learn Robot Policies. - Sumedh Sontakke; Jesse Zhang; Sébastien M. R. Arnold; Karl Pertsch; Erdem Biyik; Dorsa Sadigh; Chelsea Finn; Laurent Itti (NeurIPS, 2023)

Hierarchical Neural Program Synthesis. - Linghan Zhong; Ryan Lindeborg; Jesse Zhang; Joseph J. Lim; Shao-Hua Sun (CoRR, 2023)

LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers. - Taewook Nam; Juyong Lee; Jesse Zhang; Sung Ju Hwang; Joseph J. Lim; Karl Pertsch (CoRR, 2023)

EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data. - Jesse Zhang; Minho Heo; Zuxin Liu; Erdem Biyik; Joseph J. Lim; Yao Liu 009; Rasool Fakoor (CoRL, 2024)

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. - Zuxin Liu; Jesse Zhang; Kavosh Asadi; Yao Liu 009; Ding Zhao; Shoham Sabach; Rasool Fakoor (ICLR, 2024)

RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback. - Yufei Wang 007; Zhanyi Sun; Jesse Zhang; Zhou Xian; Erdem Biyik; David Held; Zackory Erickson (ICML, 2024)

SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling. - Jesse Zhang; Karl Pertsch; Jiahui Zhang; Joseph J. Lim (ICRA, 2024)

EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot Skills from Offline Data. - Jesse Zhang; Minho Heo; Zuxin Liu; Erdem Biyik; Joseph J. Lim; Yao Liu 009; Rasool Fakoor (CoRR, 2024)

HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation. - Yi Li 038; Yuquan Deng; Jesse Zhang; Joel Jang; Marius Memmel; Caelan Reed Garrett; Fabio Ramos 001; Dieter Fox; Anqi Li 001; Abhishek Gupta 004; Ankit Goyal 001 (ICLR, 2025)

HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation. - Yi Li 038; Yuquan Deng; Jesse Zhang; Joel Jang; Marius Memmel; Raymond Yu; Caelan Reed Garrett; Fabio Ramos 001; Dieter Fox; Anqi Li 001; Abhishek Gupta 004; Ankit Goyal 001 (CoRR, 2025)

ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations. - Jiahui Zhang; Yusen Luo; Abrar Anwar; Sumedh Anand Sontakke; Joseph J. Lim; Jesse Thomason; Erdem Biyik; Jesse Zhang (CoRR, 2025)

HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval. - Matthew Hong 002; Anthony Liang; Kevin Kim; Harshitha Rajaprakash; Jesse Thomason; Erdem Biyik; Jesse Zhang (CoRR, 2025)