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.

I am looking for a research position in academia. Please reach out if you think I could be a good fit!

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Research

My Ph.D. and postdoctoral research falls at the intersection of control theory, artificial intelligence, formal methods, and machine learning to achieve assured autonomy with guaranteed correctness and robustness, significantly reducing the manual effort involved in the design, development and deployment of autonomous systems. My research on advancing autonomous systems includes three key areas: (1) Task Specification — How do we tell autonomous systems what to do? (2) Control Synthesis — How do we ensure that robots behave as expected? (3) Verification/Certification—the process of ensuring that an algorithm meets its specified requirements.

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

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.

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

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.

This paper address the hierarchical Linear Temporal Logic (LTL) specifications in robotic planning. By breaking down complex tasks into interrelated sub-tasks within a multi-level structure, this approach offers syntactic brevity, improved interpretability, and more efficient planning.

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.

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

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.

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.

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.

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.

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.

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.

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.

Service

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