Zuxin Liu

According to our database, Zuxin Liu authored at least 40 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Safety-Aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving.

Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao

IEEE Robotics Autom. Lett., May, 2024

xLAM: A Family of Large Action Models to Empower AI Agent Systems.

Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh Murthy, Eric Hu, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

CoRR, 2024

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents.

Kexun Zhang, Weiran Yao, Zuxin Liu, Yihao Feng, Zhiwei Liu, Rithesh Murthy, Tian Lan, Lei Li, Renze Lou, Jiacheng Xu, Bo Pang, Yingbo Zhou, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong

CoRR, 2024

APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets.

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

CoRR, 2024

EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot Skills from Offline Data.

Jesse Zhang, Minho Heo, Zuxin Liu, Erdem Biyik, Joseph J. Lim, Yao Liu, Rasool Fakoor

CoRR, 2024

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases.

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

CoRR, 2024

AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System.

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

CoRR, 2024

AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning.

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

CoRR, 2024

Gradient shaping for multi-constraint safe reinforcement learning.

Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization.

Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving.

Ye Li, Hanjiang Hu, Zuxin Liu, Xiaohao Xu, Xiaonan Huang, Ding Zhao

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Feasibility Consistent Representation Learning for Safe Reinforcement Learning.

Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning from Sparse Offline Datasets via Conservative Density Estimation.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations.

Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving.

Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao

CoRR, 2023

Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving.

Ye Li, Hanjiang Hu, Zuxin Liu, Ding Zhao

CoRR, 2023

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

CoRR, 2023

Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning.

Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Shared Safety Constraints from Multi-task Demonstrations.

Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark Under Multiple Environments.

Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Jiacheng Zhu, Zuxin Liu, Wenhao Ding, Ding Zhao, Hesheng Wang

IROS, 2023

Constrained Decision Transformer for Offline Safe Reinforcement Learning.

Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao

Proceedings of the International Conference on Machine Learning, 2023

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

Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, Ding Zhao

Proceedings of the International Conference on Machine Learning, 2023

On the Robustness of Safe Reinforcement Learning under Observational Perturbations.

Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability.

Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao

CoRR, 2022

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Constrained Variational Policy Optimization for Safe Reinforcement Learning.

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao

Proceedings of the International Conference on Machine Learning, 2022

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

Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Robustness Certification of Visual Perception Models via Camera Motion Smoothing.

Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

Proceedings of the Conference on Robot Learning, 2022

2021

Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles.

Sharad Chitlangia, Zuxin Liu, Akhil Agnihotri, Ding Zhao

CoRR, 2021

Context-Aware Safe Reinforcement Learning for Non-Stationary Environments.

Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao

CoRR, 2021

Context-Aware Safe Reinforcement Learning for Non-Stationary Environments.

Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Liang Li, Ding Zhao

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method.

Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

CoRR, 2020

Delay-Aware Multi-Agent Reinforcement Learning.

Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao

CoRR, 2020

SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data.

Hasan Asy'ari Arief, Mansur Arief, Guilin Zhang, Zuxin Liu, Manoj Bhat, Ulf Geir Indahl, Håvard Tveite, Ding Zhao

IEEE Access, 2020

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

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019

A DenseNet feature-based loop closure method for visual SLAM system.

Chao Yu, Zuxin Liu, Xin-Jun Liu, Fei Qiao, Yu Wang, Fugui Xie, Qi Wei, Yi Yang

Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics, 2019

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

Zuxin Liu, Mansur Arief, Ding Zhao

Proceedings of the International Conference on Robotics and Automation, 2019

2018

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments.

Chao Yu, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei, Fei Qiao

Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018