Lishuo Pan
About me
I am Lishuo Pan, a PhD student in the ACT Lab of Brown University. I am extremely fortunate to be advised by Prof. Nora Ayanian.
Prior to Brown, I was an MS student in the GRASP Lab at the University of Pennsylvania, where I studied Computer Science and Robotics, co-advised by Prof. M. Ani Hsieh and Prof. Jianbo Shi.
Prior to UPenn, I obtained my BS with First-Class Honors from the Chinese University of Hong Kong, with a major in Statistics. During my time at CUHK, I did research under Prof. Feng Yin and Prof. Zhi-Quan Luo.
Email / CV / LinkedIn / Google Scholar
News and Updates
In reverse chronological order:
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Oct. 2025: Invited talk at SPARK Lab of MIT LIDS.
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May 2025: MPC-CBF accepted to AAMAS workshop, see you in Detroit!
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Mar. 2025: Large-scale Swarm Hierarchical Trajectory Planning accepted to AAAI workshop, see you in Philadelphia!
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Mar. 2025: Invited talk at Zhou Lab of Drexel University.
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Oct. 2024: Hierarchical Path Planning accepted to IROS 2024!
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Dec. 2023: Rapid Multi-Robot Path Planning accepted to MRS workshop, see you in Boston!
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Oct. 2023: Instance segmentation surgeon-machine interface accepted to Frontiers in Surgery 2023!
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Nov. 2022: Marlas accepted to DARS 2022!
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May 2022: Learning to Swarm accepted to ICRA 2022, see you in Philadelphia!
Selected Work
Online Learning-enhanced High Order Adaptive Control Barrier Functions Using Neural ODEs
Work in Progress
Lishuo Pan, Mattia Catellani, Lorenzo Sabattini, Nora Ayanian
Robust Trajectory Generation and Control for Quadrotor Motion Planning with Field-of-View Control Barrier Certification
Under Review at RA-L
Lishuo Pan, Mattia Catellani, Lorenzo Sabattini, Nora Ayanian
Distributed Connectivity Maintenance and Recovery for Quadrotor
Motion Planning
Under Review at ICRA 2026
Yutong Wang, Yichun Qu, Tengxiang Wang, Lishuo Pan, Nora Ayanian
Hierarchical Trajectory (Re) Planning for a Large Scale Swarm
arXiv Preprint 2025
Lishuo Pan, Yutong Wang, Nora Ayanian
Hierarchical Large Scale Multirobot Path (Re) Planning
IROS 2024 (Abu Dhabi)
Lishuo Pan, Kevin Hsu, Nora Ayanian
Marlas: Multi Agent Reinforcement Learning for Cooperated Adaptive Sampling
DARS 2022 (Montbéliard, France)
Lishuo Pan, Sandeep Manjanna, M. Ani Hsieh
Learning to Swarm with Knowledge-based Neural Ordinary Differential Equations
ICRA 2022 (Philadelphia, USA)
Tom Z Jiahao*, Lishuo Pan*, M. Ani Hsieh
Video Instance Segmentation
Instance segmentation is a fundamental task in robotic perception. The technique is particularly important in recent autonomous vehicle perception development. Recent trends move from instance segmentation on images to videos. Feature points tracking identifies the flow of detected points over frames. Combining the instance segmentation on successive static frames and feature point detection provides a physical-aware video instance segmentation solution, which does not require a huge amount of extra memory compared to the SOTA video instance segmentation.
Gaussian Processes Regression on 1-D Temporal Data
Gaussian Processes are a line of Bayesian probabilistic models, which are popular in regression with uncertainty problems. The requirement of manually selected kernel function and extensive computation prevent the framework scale to universal and large-scale problems. In this work, we aim to propose a universal kernel design to handle versatile time-series data named grid spectral mixture kernel. We rely on a set of kernels sampled from kernel hyper-parameter space and learning the weights to combine them. Using an ADMM solver, we achieve the sparse combination of kernels, which in turn provides an interpretation of the dataset.
