Zuxin Liu

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

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

Contact: zuxinl AT andrew DOT cmu DOT edu

Github / Google Scholar / LinkedIn

I am a Research Scientist at Salesforce AI Research. I received my PhD and MS degrees from Carnegie Mellon University, focusing on machine learning and robotics. During my graduate research study, I work closely with Google DeepMind, Amazon Web Services, Amazon Lab126, and Nuro. Before that, I finished my bachelor's degree with honor (President's Award) from Beihang University in 2019.

My research expertise lies at the intersection of reinforcement learning, optimization, and embodied intelligence. I am interested in how to safely deploy learning-based systems to real-world decision-making applications. Currently, I'm exploring how to efficiently utilize the power of large pretrained models to enhance downstream task-solving capabilities of autonomous agents.

  • [2024/01] Our two papers, one about efficient foundation model adaptation, and one about offline RL, are accepted by ICLR 2024!
  • [2024/01] Our paper about robustness certification is accepted by AISTATS 2024!
  • [2023/09] Our two papers for safe RL, one about versatile policy learning, and one about inverse constraint learning, are accepted by NeurIPS 2023!
  • [2023/06] Our comprehensive datasets, benchmarks, and algorithms for offline safe learning are released! Checkout our website for details!
  • [2023/05] A fast safe reinforcement learning framework is released! Checkout our GitHub repo for details!
  • [2023/04] Our two papers for safe RL, one about robustness and one about offline learning, are accepted by ICML 2023!
  • [2023/01] Our paper about observational robustness in safe RL is accepted by ICLR 2023!
  • [2022/12] Our paper about robustness in safe RL win the AI Risk Analysis Award at the 2022 NeurIPS ML Safety Workshop!
  • [2022/09] Our work about robustness certification in visual perception is accepted by CoRL 2022.
  • [2022/09] Our work about safety evaluation for self-driving vehicles is accepted by NeurIPS 2022.
  • [2022/07] Our paper about robustness in safe RL win the best paper runner-up in the SL4AD Workshop at ICML 2022!
  • [2022/07] I am glad to present my work about safe RL at Google DeepMind robotics team.
  • [2022/05] Our paper about variational inference approach for off-policy safe RL is accepted by ICML 2022!
  • [2022/05] I give a talk about recent advances in safe RL at Prof. Fei Fang's lab.
  • [2022/04] Our work about safe learning for delivery robot is featured on the front page of CMU news!
  • [2022/03] Our paper about LiDAR sensing in autonomous vehicle is accepted by CVPR 2022!
  • [2021/11] The autonomous delivery robot that we have built for one year is featured by CMU Engineering.
  • [2021/07] We win the Hackathon during my intern at Nuro! Really enjoyed to solve challenging real-world problems for self-driving.

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.
2024 ICLR.


Learning from Sparse Offline Datasets via Conservative Density Estimation

Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao.
2024 ICLR.


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.


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.


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).


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.


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.


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.


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]

Robot’s Eyes and Brain: Visual Semantic SLAM System, 2018

We developed a visual semantic SLAM system based on orb-slam2 and Mask-RCNN. The robot can build a semantic map for the environment and remember each object's position. With a speech recognition module, we can use voice command to let the robot find a particular object if he has seen before. This project won the first prize in the 2018 International Conference on Optics and Photonics(ICOPEN) 3-D Sensor Application Design Competition (1 out of 20 teams around the world).


VR Multicopter System, 2017

Do you want to fly? Our VR-Multicopter system can help you to experience the feeling of fly. We use a VR device to control orientation of a gimbal that mounted on our multicopter. The stereo camera on the gimbal will send videos back to our VR equipment in real-time. Just move your head and enjoy the view from sky! This project won the first prize in the 2017 International Design and Innovation Competition (1 out of 14 teams around the world).


Autonomous Navigation Robot, 2017

I led a team to build a mobile robot platform which could achieve autonomous navigation and obstacle avoidance based on RTAB-Map SLAM and ROS Navigation Stack. Just with one-click, the robot can autonomously navigate to wherever you want.


Automatic AI Robot System, 2017

This project is designed for ICRA DJI Robotmaster AI challenge. The robots are required to autonomously find enemy robots and hit them (shoot rubber ball). More exciting information and videos about this robot platform and the relevant robot competition can be found here.

Arduino-based Interactive Facial Expression Robot, 2016

This cute robot could make different expressions according to user’s voice command. The movements of its eyes, eyebrow, and mouth etc are fully controlled by servo motors. Most of the materials are 3D printed.

[2019] ShenYuan Medal Award (10/4000), Beihang University

[2016&2017&2018] National Scholarship (top 1%), Ministry of Education of the People's Republic of China

[2016&2017&2018] University-level Outstanding Student, Beihang University

[2018] Beijing Outstanding Student, Ministry of Education of Beijing

[2018] 28th First prize of the Feng Ru Cup Competition of Academic and Technological Works (top1%), Beihang University

[2017] Dean's Award, Beihang University

[Conference Reviewer] CVPR 2023, ICCV 2023, ICML 2022-2023, NeurIPS 2022 (top reviewer, 8%), AISTATS 2022, ICRA 2020-2023, IROS 2020-2021

[Journal Reviewer] Reviewer for Journal of Field Robotics, IEEE RA-L, IEEE T-KDE, IEEE T-VT, IEEE T-SMC, Autonomous Robots, Pattern recognition

[Program Committee] 2022 ICRA SeasonDepth Challenge, 2022 NeurIPS ML4AD Workshop, 2022 NeurIPS TSRML Workshop, 2023 AAAI UDM Workshop.

Updated on April 08st, 2020

      Zuxin Liu© 2019-2020