Zuxin Liu

Ph.D. candidate, College of Engineering, Carnegie Mellon University.

M.S. degree, Machine Learning Department, Carnegie Mellon University.

Contact: zuxinl AT andrew DOT cmu DOT edu

Amazon Web Service. Applied Scientist Intern, 2023 Summer.
    Work on foundation models for decision-making.

Amazon, Lab126 Astro Robot Team. Applied Scientist Intern, 2022 Summer.
    Work on RL for household robot exploration and planning.

Nuro, Inc. Machine Learning Research Intern. Host: Dr. Wei Liu, 2021 Summer.
    Work on safe RL for self-driving vehicle behavior planning.

Dajiang Innovations (DJI) Technology Co., Ltd. Algorithm Engineer Intern, 2017 Summer.
    Work on autonomous robot decision-making and motion planning.

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models

Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor.
Under review.

[paper]

Datasets and Benchmarks for Offline Safe Reinforcement Learning

Zuxin Liu*, Zijian Guo*, Haohong Lin, Yihang Yao, Jiacheng Zhu, Zhepeng Cen, Hanjiang Hu, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao.
Under review.

[paper] [website] [code] [code] [code]

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Zuxin Liu*, Zijian Guo*, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao.
2023 ICML.

[paper] [code]

Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data

Zuxin Liu*, Zijian Guo*, Zhepeng Cen, Yihang Yao, Hanjiang Hu, Ding Zhao.
2023 ICML.

[paper]

Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning

Yihang Yao*, Zuxin Liu*, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao.
2023 NeurIPS.

[paper]

Learning Shared Safety Constraints from Multi-task Demonstrations

Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu.
2023 NeurIPS.
2023 ICML ILHF Workshop (oral).

[paper]

Learning to Explore (L2E): Deep Reinforcement Learning-based Autonomous Exploration for Household Robot

Zuxin Liu, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen.
2023 RSS Robot Representations Workshop.

[paper]

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li and Ding Zhao.
2023 ICLR.
2022 ICML Safe Learning for Autonomous Driving Workshop (Best Paper Runner-up).
2022 NeurIPS ML Safety Workshop (AI Risk Analysis Award).

[arXiv] [website] [code]

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalization

Mengdi Xu*, Zuxin Liu*, Peide Huang*, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao.
Under review.

[arXiv]

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li and Ding Zhao.
2022 ICML.

[arXiv] [code]

Robustness Certification of Visual Perception Models via Camera Motion Smoothing

Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao.
2022 CoRL.

[arXiv] [code]

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li.
2022 NeurIPS.

[arXiv] [website]

Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

Hanjiang Hu^*, Zuxin Liu^*, Sharad Chitlangia, Akhil Agnihotri and Ding Zhao.
2022 CVPR.

[arXiv] [code]

Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert and Ding Zhao.
2021 ICLR Workshop on Security and Safety in Machine Learning Systems.

[arXiv] [code]

Context-Aware Safe Reinforcement Learning in Non-Stationary Environments

Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding and Ding Zhao.
2021 ICRA.

[arXiv] [code]

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert and Ding Zhao.
2020 IROS.

[arXiv]

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen and Ding Zhao
2020 NeurIPS.

[arXiv] [code]

SAnE: Smart Annotation and Evaluation tools for point cloud data

Hasan Arief, Mansur Arief, Guilin Zhang, Zuxin Liu, Manoj Bhat, and Ding Zhao
IEEE Access.

[arXiv] [code]

Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

Zuxin Liu, Mansur Arief and Ding Zhao
2019 ICRA.

[arXiv] [code]

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Chao Yu, Zuxin Liu and Fei Qiao
2018 IROS (ranked as the most popular presentation by INFOVAYA)

[arXiv] [code]