Kevin Lu

Contact: kzl@berkeley.edu
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I'm interested in training universal deep learning models. Some of my past work has studied sequence modeling for reinforcement learning and cross-modal transfer. For a recent summary of my work, see the slides for my talk Towards a Universal Paradigm for Decision Making.

I was previously an AI resident at FAIR, advised by Amy Zhang and Yuandong Tian. I graduated with a B.S. in Electrical Engineering and Computer Science from UC Berkeley in 2021, where I did undergraduate research as part of the Robot Learning Lab advised by Igor Mordatch and Pieter Abbeel. Note that I am currently taking a leave from research, and may be slow to answer inquiries.

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Highlighted Papers
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas*, Igor Mordatch*
Neural Information Processing Systems (NeurIPS), 2021
Official: arXiv / website / poster / tweet / code
Press: The Batch article / SyncedReview article / The Gradient article / Yannic Kilcher video / Eindhoven RL seminar
Pretrained Transformers as Universal Computation Engines
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
AAAI Conference on Artificial Intelligence, 2022
Official: arXiv / blog / poster / tweet / code
Press: The Batch article / VentureBeat article / TWIML podcast / Yannic Kilcher video
All Papers
Pretraining for Language-Conditioned Imitation with Transformers
Aaron (Louie) Putterman, Kevin Lu, Igor Mordatch, Pieter Abbeel
NeurIPS Offline Reinforcement Learning Workshop, 2021
Official: paper / code
URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin*, Denis Yarats*, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel
Neural Information Processing Systems (NeurIPS), 2021
Official: arXiv / blog / tweet / code
Press: Import AI article
Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
International Conference on Learning Representations (ICLR), 2021
Official: arXiv / website / oral / poster / tweet / code
Efficient Empowerment Estimation for Unsupervised Stabilization
Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin
International Conference on Learning Representations (ICLR), 2021
Official: arXiv / website / code
Adaptive Online Planning for Continual Lifelong Learning
Kevin Lu, Igor Mordatch, Pieter Abbeel
NeurIPS Deep Reinforcement Learning Workshop, 2019
Official: arXiv / website / oral / poster / code
Press: Alignment Newsletter article
Teaching
EECS

EECS 126: Probability and Random Processes
Head Teaching Assistant: Spring 2021, Fall 2020
Teaching Assistant: Spring 2020, Fall 2019

CS 70: Discrete Math and Probability
Reader: Spring 2019


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