Xuanlin (Simon) Li

I am a 3rd-year PhD student at UCSD CSE, advised by Prof. Hao Su (2021-). Previously I was an undergraduate majoring in Mathematics and Computer Science at UC Berkeley (2017-2021). I was also an undergraduate research assistant at Berkeley Artificial Intelligence Research, where I was advised by Prof. Trevor Darrell.

Resume (Sep.23)  /  GitHub  /  Google Scholar  /  LinkedIn  /  Twitter

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I am primarily interested in Embodied AI, Vision-Language, and Robotics. In particular, I'm interested in building (2D/3D) vision-language models and policies with generic perception and reasoning capabilities. When combined with large-scale robotic learning systems, this empowers robots to acquire generalizable skills and excel in diverse task scenarios.
(* = equal contribution)

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Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

Xuanlin Li*, Yunhao Fang*, Minghua Liu, Zhan Ling, Zhuowen Tu, Hao Su
International Conference on Computer Vision (ICCV) 2023
arxiv / code / poster /

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Deductive Verification of Chain-of-Thought Reasoning

Zhan Ling*, Yunhao Fang*, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, Hao Su
Neural Information Processing Systems (NeurIPS) 2023
arxiv / code /

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OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

Minghua Liu*, Ruoxi Shi*, Kaiming Kuang*, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su
Neural Information Processing Systems (NeurIPS) 2023
arxiv / website / code /

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Situated Real-time Interaction with a Virtually Embodied Avatar

Sunny Panchal, Guillaume Berger, Antoine Mercier, Cornelius Bohm, Florian Dietrichkeit, Xuanlin Li, Reza Pourreza, Pulkit Madan, Apratim Bhattacharyya, Mingu Lee, Mark Todorovich, Ingo Bax, Roland Memisevic
CVPR 2023 Embodied AI Workshop (Preprint)
paper /

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On the Efficacy of 3D Point Cloud Reinforcement Learning

Zhan Ling*, Yunchao Yao*, Xuanlin Li, Hao Su
arxiv / code /

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Reparameterized Policy Learning for Multimodal Trajectory Optimization

Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su
International Conference on Machine Learning (ICML) 2023 (Oral)
arxiv / website / code /

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Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds

Minghua Liu*, Xuanlin Li*, Zhan Ling*, Yangyan Li, Hao Su
Conference on Robot Learning (CoRL) 2022
arxiv / website / video / code /

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ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu†, Fanbo Xiang†, Xuanlin Li*, Zhan Ling*, Xiqiang Liu*, Tongzhou Mu*, Yihe Tang*, Stone Tao*, Xinyue Wei*, Yunchao Yao*, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su
ICLR 2023
arxiv / website / code / implementation /

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ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations

Tongzhou Mu*, Zhan Ling*, Fanbo Xiang*, Derek Yang*, Xuanlin Li*, Stone Tao, Zhiao Huang, Zhiwei Jia, Hao Su
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2021
arxiv / website / video / code / implementation /

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Discovering Non-Monotonic Autoregressive Orderings with Variational Inference

Xuanlin Li*, Brandon Trabucco*, Dong Huk Park, Yang Gao, Michael Luo, Sheng Shen, Trevor Darrell
International Conference on Learning Representations (ICLR) 2021
arxiv / video_transcripts / code / poster / slides /

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Improving Policy Optimization with Generalist-Specialist Learning

Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, Hao Su
International Conference on Machine Learning (ICML) 2022
arxiv / website / code /

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Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control

Zhuang Liu*, Xuanlin Li*, Bingyi Kang, Trevor Darrell
International Conference on Learning Representations (ICLR) 2021 (Spotlight)
arxiv / video / code / poster / slides /

Other Projects

These include coursework, side projects and unpublished research work.

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Inferring the Optimal Policy using Markov Chain Monte Carlo

Brandon Trabucco, Albert Qu, Xuanlin Li, Ganeshkumar Ashokavardhanan
Berkeley EECS 126 (Probability and Random Processes)
arxiv /

Final course project for EECS 126 (Probability and Random Processes) in Fall 2018.


Qualcomm AI Research, Research Intern, Mar. 2023 - Sep. 2023
Berkeley Artificial Intelligence Research, Undergraduate Research Assistant, 2019 - 2021


Honors and Awards

Jacobs School of Engineering PhD Fellowship, UC San Diego CSE, 2021
Arthur M. Hopkin Award, UC Berkeley EECS, 2021
EECS Honors Program & Mathematics Honors Program, UC Berkeley

Design and source code from Jon Barron's website