Zuxin Liu

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 54

Top Topics

Reinforcement Learning

Autonomous Driving

Dynamic Environments

Top Venues

Publications

Jesse Zhang, Minho Heo, Zuxin Liu, Erdem Biyik, Joseph J. Lim, Yao Liu, Rasool Fakoor
EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot Skills from Offline Data. CoRR (2024)
Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. ICLR (2024)
Ye Li, Hanjiang Hu, Zuxin Liu, Xiaohao Xu, Xiaonan Huang, Ding Zhao
Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving. ICRA (2024)
Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization. ICRA (2024)
Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao
Learning from Sparse Offline Datasets via Conservative Density Estimation. ICLR (2024)
Zuxin Liu, Thai Hoang, Jianguo Zhang, Ming Zhu, Tian Lan, Shirley Kokane, Juntao Tan, Weiran Yao, Zhiwei Liu, Yihao Feng, Rithesh Murthy, Liangwei Yang, Silvio Savarese, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong
APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets. CoRR (2024)
Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao
Gradient shaping for multi-constraint safe reinforcement learning. L4DC (2024)
Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
Safety-Aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving. IEEE Robotics Autom. Lett. 9 (5) (2024)
Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao
Feasibility Consistent Representation Learning for Safe Reinforcement Learning. CoRR (2024)
Rithesh Murthy, Liangwei Yang, Juntao Tan, Tulika Manoj Awalgaonkar, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, Jianguo Zhang, Zhiwei Liu, Shirley Kokane, Zuxin Liu, Ming Zhu, Huan Wang, Caiming Xiong, Silvio Savarese
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases. CoRR (2024)
Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao
Learning from Sparse Offline Datasets via Conservative Density Estimation. CoRR (2024)
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations. AISTATS (2024)
Jianguo Zhang, Tian Lan, Rithesh Murthy, Zhiwei Liu, Weiran Yao, Juntao Tan, Thai Hoang, Liangwei Yang, Yihao Feng, Zuxin Liu, Tulika Manoj Awalgaonkar, Juan Carlos Niebles, Silvio Savarese, Shelby Heinecke, Huan Wang, Caiming Xiong
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning. CoRR (2024)
Zhiwei Liu, Weiran Yao, Jianguo Zhang, Liangwei Yang, Zuxin Liu, Juntao Tan, Prafulla Kumar Choubey, Tian Lan, Jason Wu, Huan Wang, Shelby Heinecke, Caiming Xiong, Silvio Savarese
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System. CoRR (2024)
Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization. CoRR (2023)
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations. CoRR (2023)
Ye Li, Hanjiang Hu, Zuxin Liu, Ding Zhao
Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving. CoRR (2023)
Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning. CoRR (2023)
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao
Constrained Decision Transformer for Offline Safe Reinforcement Learning. CoRR (2023)
Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. CoRR (2023)
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao
On the Robustness of Safe Reinforcement Learning under Observational Perturbations. ICLR (2023)
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao
Constrained Decision Transformer for Offline Safe Reinforcement Learning. ICML (2023)
Zuxin Liu, Zijian Guo, Haohong Lin, Yihang Yao, Jiacheng Zhu, Zhepeng Cen, Hanjiang Hu, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao
Datasets and Benchmarks for Offline Safe Reinforcement Learning. CoRR (2023)
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, Ding Zhao
Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data. ICML (2023)
Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Steven Z. Wu
Learning Shared Safety Constraints from Multi-task Demonstrations. NeurIPS (2023)
Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving. CoRR (2023)
Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning. NeurIPS (2023)
Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Jiacheng Zhu, Zuxin Liu, Wenhao Ding, Ding Zhao, Hesheng Wang
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark Under Multiple Environments. IROS (2023)
Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu
Learning Shared Safety Constraints from Multi-task Demonstrations. CoRR (2023)
Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning. CoRR (2023)
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
Robustness Certification of Visual Perception Models via Camera Motion Smoothing. CoRR (2022)
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
Robustness Certification of Visual Perception Models via Camera Motion Smoothing. CoRL (2022)
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li, Ding Zhao
Constrained Variational Policy Optimization for Safe Reinforcement Learning. ICML (2022)
Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. CoRR (2022)
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao
On the Robustness of Safe Reinforcement Learning under Observational Perturbations. CoRR (2022)
Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability. CoRR (2022)
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
Constrained Variational Policy Optimization for Safe Reinforcement Learning. CoRR (2022)
Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving. CVPR (2022)
Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. NeurIPS (2022)
Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments. CoRR (2021)
Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Liang Li, Ding Zhao
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments. ICRA (2021)
Sharad Chitlangia, Zuxin Liu, Akhil Agnihotri, Ding Zhao
Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles. CoRR (2021)
Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. CoRR (2020)
Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao
Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method. CoRR (2020)
Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments. CoRR (2020)
Hasan Asy'ari Arief, Mansur Arief, Guilin Zhang, Zuxin Liu, Manoj Bhat, Ulf Geir Indahl, Håvard Tveite, Ding Zhao
SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data. IEEE Access 8 (2020)
Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. NeurIPS (2020)
Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao
Delay-Aware Multi-Agent Reinforcement Learning. CoRR (2020)
Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments. IROS (2020)
Chao Yu, Zuxin Liu, Xin-Jun Liu, Fei Qiao, Yu Wang, Fugui Xie, Qi Wei, Yi Yang
A DenseNet feature-based loop closure method for visual SLAM system. ROBIO (2019)
Zuxin Liu, Mansur Arief, Ding Zhao
Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach. ICRA (2019)
Chao Yu, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei, Fei Qiao
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments. IROS (2018)
Zuxin Liu, Mansur Arief, Ding Zhao
Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach. CoRR (2018)
Chao Yu, Zuxin Liu, Xinjun Liu, Fugui Xie, Yi Yang, Qi Wei, Fei Qiao
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments. CoRR (2018)