Photo by Katie Kackenmeister

Guangyi Liu

Hi, welcome to my website. I obtained my Ph.D. at the Autonomous and Intelligent Robotics (AIR) Laboratory, Department of Mechanical Engineering, Lehigh University, working under the advisory of Prof. Nader Motee. Before joining Lehigh, I obtained my B.E. degree at Beijing Institute of Technology. My research interests are risk and robustness analysis in networked control systems and perception systems. I have served as a workshop organizer in ACC 2023 and organized three invited sessions at ACC 2023 and ACC 2024.

Office: BC 105, Building C        Email: gliu@lehigh.edu           Google Scholar          CV: 2024/03

Research:

Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot  Navigation and 3D Scene Understanding with FisherRF 

(Paper: C12) We introduce a novel approach to enhance robot navigation safety and improve 3D scene understanding by extending beyond conventional uncertainty-based methods. 

Video

Risk analysis of cascading failures in networked control systems with autonomous vehicles and multi-agent rendezvous.

(Papers: J3, C5, C6, C7, C8) We investigate and quantify how the existing failure (e.g., inter-vehicle collisions) will propagate through the network. We also show how communication time-delay and input uncertainty will pose fundamental limits and trade-offs on the risk of cascading collisions.

Data-Driven Distributionally Robust Mitigation of Risk of Cascading Collisions

(Papers: C10, C2) We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed discrete-time Linear Time-Invariant (LTI) systems, which involves formulating a distributionally robust finite-horizon optimal control problem while satisfying a set of distributionally chances constraints on cascading failures.

Symbolic  Perception Risk  in Autonomous Driving

(Papers: C9, C3) We develop a novel framework to assess the risk of misperception in a traffic sign classification task in the presence of exogenous noise. 

Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks

(Papers: J2) We develop a framework to quantify systemic risk measures in a class of Wide-Area-Control (WAC) laws in power networks in the presence of noisy and time-delayed sensory data, and reveal the effect of network parameters, information flow in WAC architecture, statistics of noise, and time-delays are characterized.

Robust Analysis of Multi-agent Map Classification with RNN

(Papers: C4, C1, P1 - P4) For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.

News:


Journal Publications:

 [J2]. Guangyi Liu, Christoforos Somarakis, and Nader Motee. "Risk of Cascading Collisions in Network of Vehicles with Delayed Communication", (submitted to IEEE-TAC, under review). 

 [J1]. Christoforos Somarakis, Guangyi Liu, and Nader Motee. "Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks.", IEEE-TAC. 


Conference Proceedings:

[C12]. Guangyi Liu*, Wen Jiang*, Boshu Lei*, Vivek Pandey, Kostas Daniilidis and Nader Motee. "Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF''  (* equal contribution) (submitted to IROS 2024)

[C11].Vivek Pandey*, Arash Amini*, Guangyi Liu, Ufuk Topcu, Qiyu Sun, Kostas Daniilidis, Nader Motee. "Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation.'' (* equal contribution) (submitted to IROS 2024)

[C10]. Guangyi Liu, Arash Amini, Vivek Pandey, and Nader Motee. "Data-Driven Distributionally Robust Mitigation of Risk of  Cascading Failures.", 2024 American Control Conference (ACC).

[C9]. Guangyi Liu, Disha Kamale, Cristian-Ioan Vasile, and Nader Motee. "Symbolic Perception Risk in Autonomous Driving.", 2023 American Control Conference (ACC), May 31 - June 2, San Diego, CA, USA.

[C8]. Guangyi Liu, Vivek Pandey, Christoforos Somarakis, and Nader Motee. "Cascading Waves of Fluctuation in Time-delay Multi-agent Rendezvous", 2023 American Control Conference (ACC), May 31 - June 2, San Diego, CA, USA.

[C7]. Guangyi Liu, Christoforos Somarakis, and Nader Motee. "Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles.", The 61st IEEE Conference on Decision and Control (CDC), December, 2022, Cancun, Mexico.

[C6]. Guangyi Liu, Vivek Pandey, Christoforos Somarakis, and Nader Motee. "Risk of Cascading Failures in Multi-agent Rendezvous with Communication Time Delay.", 2022 American Control Conference (ACC), June 8-10, Atlanta, GA, USA.

[C5]. Guangyi Liu, Christoforos Somarakis, and Nader Motee. "Risk of Cascading Failures in Time-Delayed Vehicle Platooning.", The 60th IEEE Conference on Decision and Control (CDC), December 13-15, 2021, Austin, Texas, USA.

[C4]. Guangyi Liu, Arash Amini, Martin Takáč, and Nader Motee. "Classification-Aware Path Planning of Network of Robots." THE 15TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED AUTONOMOUS ROBOTIC SYSTEMS 2021 (DARS 2021), June 2021, Kyoto, Japan.

[C3]. Arash Amini, Guangyi Liu, Vivek Pandey, and Nader Motee. "Impact of Misperception on Emergence of Risk in Platoon of Autonomous Vehicles", CDC 2023.

[C2]. Vivek Pandey, Guangyi Liu, Arash Amini, and Nader Motee. "Quantification of Distributionally Robust Risk of Cascade of Failures in Platoon of Vehicles.", CDC 2023.

[C1]. Arash Amini, Guangyi Liu, and Nader Motee. "Robust Learning of  Recurrent Neural Networks in Presence of Exogenous Noise.", The 60th IEEE conference on Decision and Control (CDC), December 13-15, 2021, Austin, Texas, USA. 

Preprints:

[P4]. Guangyi Liu, Arash Amini, and Nader Motee. "Risk of Misclassification in Recurrent Neural Networks."

[P3]. Guangyi Liu, Arash Amini, Martin Takáč, and Nader Motee. "Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input.".

[P2]. Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee. "Distributed Map Classification using Local Observations" arXiv preprint arXiv:2012.10480 (2020). 

[P1]. Mousavi, Hossein K., Guangyi Liu, Weihang Yuan, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee. "A layered architecture for active perception: Image classification using deep reinforcement learning." arXiv preprint arXiv:1909.09705 (2019). 

Posters and Extended Abstracts:


Selected Talks: