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.
Iām seeking a research position in academia or industry. Feel free to reach out if you think I could be a good fit!
Email /
Scholar /
Github
|
|
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:
- Task Specification ā How can we effectively specify tasks for autonomous systems?
- Control Synthesis ā How can we ensure robots behave as intended?
- 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).
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|