Xusheng Luo

I'm currently a Posdoctoral Fellow at Intelligent Control Lab of Carnegie Mellon University, working with Dr. Changliu Liu, starting from April 2023. I received the Ph.D. degree in Mechanical Engineering and Materials Science from Duke University in December 2020, under the supervision of Dr. Michael M. Zavlanos. Prior to it, I received the B.Eng. and M.S.E. degrees in Aerospace Engineering from the Harbin Institute of Technology, China, in 2015 and 2017, respectively.

Iā€™m seeking a research position in academia or industry. Feel free to reach out if you think I could be a good fit!

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Research

I am a robotics and AI researcher specializing in developing intelligent and reliable autonomous systems. My work combines symbolic AI for high-level task planning with optimization-based motion planning, integrating both model-based and data-driven techniques. My research focuses on three fundamental questions:

  1. Task Specification ā€” How can we effectively specify tasks for autonomous systems?
  2. Control Synthesis ā€” How can we ensure robots behave as intended?
  3. Verification/Certification ā€” How do we formally verify that an algorithm meets its specified requirements?
I apply these approaches to autonomous driving, mobile robots, and robotic arms, advancing their autonomy, adaptability, and real-world reliability.

Representative papers are highlighted (* denotes equal contribution).
safs_small Hierarchical Temporal Logic Task and Motion Planning for Multi-Robot Systems
Zhongqi Wei*, Xusheng Luo*, Changliu Liu
2025
paper

This work addresses the TAMP problem in multi-robot settings where tasks are specified using expressive hierarchical temporal logic and task assignments are not pre-determined.

safs_small Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods
Xusheng Luo, Tianhao Wei, Simin Liu, Ziwei Wang, Luis Mattei-Mendez, Taylor Loper, Joshua Neighbor, Casidhe Hutchison, Changliu Liu
ACM Transaction on Cyber-Physical Systems, 2025
paper

This is the first study to certify the robustness of large-scale, keypoint-based pose estimation given images in real-world scenarios.

safs_small ModelVerification. jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Luca Marzari, Kai S. Yun, Hanjiang Hu, Peizhi Niu, Xusheng Luo, Changliu Liu
arXiv , 2024
paper

This paper presents ModelVerification.jl (MV), the first comprehensive, cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying different types of DNNs and safety specifications.

safs_small NL2HLTL2PLAN: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation
Xusheng Luo*, Shaojun Xu*, Yutong Huang, Letian Leng, Ruixuan Liu, Changliu Liu
CoRL Workshop on Learning Effective Abstractions for Planning (LEAP) , 2023
arXiv , 2024
paper

We proposed a neuro-symbolic paradigm of extracting task hierarchies from human instructions to facilitate multi-robot planning for complex, long-horizon tasks.

safs_small Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Xusheng Luo, Changliu Liu
submitted to Transaction on Robotics (conditionally accepted) , 2024
paper / code

By leveraging the intrinsic structure of tasks, this paper introduced a hierarchical structure to LTL specifications. We employ a search-based approach to synthesize plans for a multi-robot system, accomplishing simultaneous task allocation and planning.

safs_small Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Xusheng Luo, Shaojun Xu, Ruixuan Liu, Changliu Liu
IEEE Robotics and Automation Letters (RA-L), 2024
IROS Workshop on Formal Methods Techniques in Robotics Systems: Design and Control , 2023
video / paper / code

This paper proposes a a decomposition-based hierarchical framework for robotic takss under hierarchical Linear Temporal Logic (LTL) specifications.

safs_small Simulation-aided Learning from Demonstration for Robotic LEGO Construction
Ruixuan Liu, Alan Chen, Xusheng Luo, Changliu Liu
arXiv , 2023
paper / video

This paper introduces a simulation-aided learning from demonstration (SaLfD) framework, enabling robots to learn and execute LEGO assembly and disassembly tasks from human demonstrations, successfully implemented and tested on a FANUC LR-mate 200id/7L robot.

safs_small Temporal Logic Task Allocation in Heterogeneous Multi-robot Systems
Xusheng Luo, Michael M Zavlanos
IEEE Transactions on Robotics (T-RO), 2022
paper / extended version / code

This paper addresses the challenge of allocating tasks, defined by global Linear Temporal Logic (LTL) specifications, to diverse teams of robots.

safs_small Formal Verification of Stochastic Systems with ReLU Neural Network Controller
Shiqi Sun, Yan Zhang, Xusheng Luo, Panagiotis Vlantis, Miroslav Pajic, Michael M Zavlanos
ICRA, 2022
paper

This paper tackles the challenge of formal safety verification for stochastic cyber-physical systems (CPS) that use ReLU neural network controllers, aiming to identify initial states that ensure the system remains safe within a certain time frame.

safs_small An abstraction-free Method for Multi-robot Temporal Logic Optimal Control Synthesis
Xusheng Luo, Yiannis Kantaros, Michael M Zavlanos
IEEE Transactions on Robotics (T-RO), 2021
paper / code

This article introduces a new sampling-based linear temporal logic (LTL) planning algorithm that builds trees to explore the state-space, avoiding the need for complex discrete abstractions of robot mobility.

safs_small An optimal Graph-Search Method for Secure State Estimation
Xusheng Luo, Miroslav Pajic, Michael M Zavlanos
Automatica, 2021
paper

This paper proposes a new optimal graph-search algorithm designed for large-scale, linear time-invariant systems, which efficiently identifies malicious attacks and accurately estimates states.

safs_small Human-in-the-loop Robot Planning with Non-contextual Bandit Feedback
Yijie Zhou, Yan Zhang, Xusheng Luo, Michael M Zavlanos
CDC, 2021
paper

This paper addresses the challenge of robot navigation in human-populated environments, aiming to create trajectories that are collision-free, dynamically feasible, and maximize human satisfaction by being responsive to human needs and avoiding discomfort.

safs_small Socially-aware Robot Planning via Bandit Human Feedback
Xusheng Luo*, Yan Zhang*, Michael M Zavlanos
ICCPS, 2020
paper

This paper presents a novel framework for designing socially-aware robotic trajectories in human-populated environments, defining socially-awareness as the avoidance of human discomfort.

Single-agent Indirect Herding of Multiple Targets using Metric Temporal Logic Switching
Duc Le, Xusheng Luo, Leila J. Bridgeman, Michael M Zavlanos, E. Dixon
CDC, 2020
paper

The paper addresses the single-agent indirect herding problem, focusing on a herder agent's task to guide a group of target agents to a specific goal.

safs_small Transfer Planning for Temporal Logic Tasks
Xusheng Luo, Michael M Zavlanos
CDC, 2019
paper

This paper introduces an optimal control synthesis algorithm for Linear Temporal Logic (LTL) tasks, utilizing a novel approach that leverages experience from previous similar tasks.

Reviewer

Reviewer for T-RO, T-ASE, T-CNS, RA-L, L-CSS, RSS, ICRA, IROS, ACC, ICCPS, UR.


Design and source code from Jon Barron's website.