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I work on reinforcement learning, decision making, and artificial intelligence as part of the Robot Learning Lab where I am fortunate to work with Igor Mordatch, Aditya Grover, and Pieter Abbeel. I recently finished my undergraduate studies at UC Berkeley where I studied Electrical Engineering and Computer Science. In the fall, I will be starting as an AI resident at Facebook AI Research for one year. Email / CV / Google Scholar / Twitter / Github |
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I am broadly interested in artificial intelligence. I am particularly interested in making learning more autonomous, such as enabling agents to learn in reset-free, nonstationary, or unsupervised settings; or by incorporating more sources of supervision, such as offline or multimodal data. |
 
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Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas*, Igor Mordatch* arXiv preprint, 2021 arXiv / website / code unofficial: press / video (by Yannic Kilcher) We show that a simple transformer trained with a sequence modeling objective can perform competitively with strong specialized offline RL methods. |
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Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch arXiv preprint, 2021 arXiv / blog / code unofficial: press / podcast / video (by Yannic Kilcher) We show that a transformer pretrained on natural language can, without finetuning of the self-attention and feedforward layers, match the performance of a transformed fully trained on a downstream non-language modality. |
 
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Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch International Conference on Learning Representations, 2021 NeurIPS Deep RL Workshop, 2020 (Contributed Talk) ICLR Never-Ending Reinforcement Learning Workshop, 2021 (Invited Paper) arXiv / website / oral / poster / code We show planning over a space of skills is a key component of successful reset-free lifelong learning, avoiding sink states, improving stability, and increasing learning signal. |
 
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Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin International Conference on Learning Representations, 2021 arXiv / website / code We design a new unbiased empowerment estimator and show it represents empowerment more faithfully than traditional variational mutual information algorithms. |
 
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Kevin Lu, Igor Mordatch, Pieter Abbeel NeurIPS Deep RL Workshop, 2019 (Contributed Talk) arXiv / website / oral / poster / code Tackling reset-free learning in dynamically changing worlds by combining model-based planning with model-free learning, utilizing an expensive planner only when necessary. |
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EECS 126: Probability and Random Processes
CS 70: Discrete Math and Probability |
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