ARCS Lab Members
Faculty
Jiaoyang received a Ph.D. degree in Computer Science at University of Southern California in 2022 and a B.Eng. degree from Department of Automation at Tsinghua University in 2017. She is interested in a variety of topics related to Artificial Intelligence and optimization, such as combinatorial algorithms, heuristic search, scheduling and planning for robotics and transportation.
Ph.D. Students
Philip received a BASc in Engineering Science in 2021 and an MSc in Computer Science in 2023 from the University of Toronto. His research interests include multi-robot collaboration, task and motion planning, and machine learning. A long-term goal of his research is to develop algorithms that enable individual robots and robot teams to accomplish complex and long-horizon tasks in dynamic and uncertain environments.
Publications
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Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning.
Philip Huang, Yorai Shaoul, Jiaoyang Li. -
APEX-MR: Multi-Robot Asynchronous Planning and Execution for Cooperative Assembly.
Philip Huang*, Ruixuan Liu*, Shobhit Aggarwal, Changliu Liu, Jiaoyang Li.
Yorai earned a B.Sc. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2021. His research interests span multi-robot task and motion planning, robotic manipulation, statistical learning, and computer vision. He works to harness insights from established planning algorithms to address messy real-world challenges in manipulation.
Publications
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Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks.
Rishi Veerapaneni, Ho Kwan Alvin Tang*, Yidai Cen*, Haodong He*, Sophia Zhao*, Viraj Shah*, Ziteng Ji*, Gabriel Olin, Jon Arrizabalaga, Yorai Shaoul, Jiaoyang Li, Maxim Likhachev. -
Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning.
Philip Huang, Yorai Shaoul, Jiaoyang Li. -
Multi-Robot Motion Planning with Diffusion Models.
(Spotlight)
Yorai Shaoul*, Itamar Mishani*, Shivam Vats*, Jiaoyang Li, Maxim Likhachev. -
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality.
Yorai Shaoul*, Rishi Veerapaneni*, Maxim Likhachev, Jiaoyang Li. -
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences.
(Best Student Paper)
Yorai Shaoul*, Itamar Mishani*, Maxim Likhachev, Jiaoyang Li.
Rishi works with Professors Maxim Likhachev and Jiaoyang Li in the Robotics Institute at CMU and is supported by the NSF Graduate Research Fellowship. His specific research interest is in (1) designing better heuristic search algorithms, (2) multi-agent motion planning and coordination (e.g. MAPF), and (3) combining search with machine learning. Previously, he double majored in EECS and Applied Math at UC Berkeley and was very active in teaching (EE16A, CS188, CS170 x2).
Publications
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Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks.
Rishi Veerapaneni, Ho Kwan Alvin Tang*, Yidai Cen*, Haodong He*, Sophia Zhao*, Viraj Shah*, Ziteng Ji*, Gabriel Olin, Jon Arrizabalaga, Yorai Shaoul, Jiaoyang Li, Maxim Likhachev. -
BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs.
(Oral)
Yifan Su, Rishi Veerapaneni, Jiaoyang Li. -
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees.
(Oral)
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan, Jiaoyang Li, Maxim Likhachev. -
Real-Time LaCAM for Real-Time MAPF.
Runzhe Liang*, Rishi Veerapaneni*, Daniel Harabor, Jiaoyang Li, Maxim Likhachev. -
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding.
(Best Paper on Multi-Robot Systems; Best Student Paper)
He Jiang*, Yutong Wang*, Rishi Veerapaneni, Tanishq Harish Duhan, Guillaume Adrien Sartoretti, Jiaoyang Li. -
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF.
Rishi Veerapaneni*, Arthur Jakobsson*, Kevin Ren, Samuel Kim, Jiaoyang Li, Maxim Likhachev. -
Windowed MAPF with Completeness Guarantees.
Rishi Veerapaneni, Muhammad Suhail Saleem, Jiaoyang Li, Maxim Likhachev. -
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities.
(Winner of 2023 League of Robot Runners)
He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li. -
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality.
Yorai Shaoul*, Rishi Veerapaneni*, Maxim Likhachev, Jiaoyang Li. -
MAPF in 3D Warehouses: Dataset and Analysis.
Qian Wang*, Rishi Veerapaneni*, Yu Wu, Jiaoyang Li, Maxim Likhachev. -
Improving Learnt Local MAPF Policies with Heuristic Search.
Rishi Veerapaneni*, Qian Wang*, Kevin Ren*, Arthur Jakobsson, Jiaoyang Li, Maxim Likhachev. -
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution.
(Oral)
Yifan Su, Rishi Veerapaneni, Jiaoyang Li.
Jingtian received a B.Sc. from Zhejiang University in 2020 and an M.Sc. from the Carnegie Mellon University in 2023. His research interests include multi-robot coordination, autonomous exploration, and Multi-Agent Path Finding.
Publications
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Multi-agent Motion Planning for Differential Drive Robots Through Stationary State Search.
Jingtian Yan, Jiaoyang Li. -
Multi-Agent Motion Planning With Bézier Curve Optimization Under Kinodynamic Constraints.
Jingtian Yan, Jiaoyang Li.
Yulun received a B.Sc. and an M.Sc. in Computer Science from the University of Southern California in 2021 and 2022. His research interests include human-robot collaboration, multi-robot coordination, evolutionary algorithms, and quality diversity optimization. As a long-term goal, his research focuses on bringing Quality Diversity Optimization and Evolutionary Optimization to Robotics, expanding their applicability and scalability.
Publications
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QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps.
Cheng Qian*, Yulun Zhang*, Varun Bhatt, Matthew C. Fontaine, Stefanos Nikolaidis, Jiaoyang Li. -
Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems.
Yulun Zhang, Alexandre O. G. Barbosa, Federico Pecora, Jiaoyang Li. -
Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding.
Hongzhi Zang*, Yulun Zhang*, He Jiang, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li. -
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding.
Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. -
Scalable Mechanism Design for Multi-Agent Path Finding.
Paul Friedrich*, Yulun Zhang*, Michael Curry, Ludwig Dierks, Stephen McAleer, Jiaoyang Li, Tuomas Sandholm, Sven Seuken. -
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities.
(Winner of 2023 League of Robot Runners)
He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li. -
Arbitrarily Scalable Environment Generators via Neural Cellular Automata.
Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. -
Multi-Robot Coordination and Layout Design for Automated Warehousing.
Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li.
Masters Students
Srikar earned a B.S in Computer Science from the University of Virginia in 2025. His research interests include traditional and learning-based approaches to multi-robot planning and cooperative task planning. His goal is to coordinate mobile robots to complete complex tasks faster and more effectively than a single robot could.
He Jiang received a Bachelor's degree in Computer Science and Engineering from Shanghai Jiaotong University in 2017 and another Master's degree in Control Science and Engineering from Tsinghua University in 2020. Then he worked for more than 1 year at Hangzhou High-Tech Zone (Binjiang), China. He is interested in planning and his current research focuses on Multi-Agent Systems. Hopefully, he can enjoy his days at CMU and do some meaningful work. He is also interested in Soccer, Poker, Chinese Chess & Nintendo Switch, by the way.
Publications
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Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding.
(Best Paper on Multi-Robot Systems; Best Student Paper)
He Jiang*, Yutong Wang*, Rishi Veerapaneni, Tanishq Harish Duhan, Guillaume Adrien Sartoretti, Jiaoyang Li. -
Speedup Techniques for Switchable Temporal Plan Graph Optimization.
(Oral)
He Jiang, Muhan Lin, Jiaoyang Li. -
Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding.
Hongzhi Zang*, Yulun Zhang*, He Jiang, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li. -
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding.
Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. -
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities.
(Winner of 2023 League of Robot Runners)
He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li.
Mehul received a B.Tech in Mechanical Engineering from the National Institute of Technology, Durgapur in 2024. His research interests encompass physics-aware task-chaining and planning in dynamic environments, with humans-in-the-loop.
Vaibhav received Bachelor's degrees in Computer Science and Mathematics from the University of Maryland, College Park in 2025. His research interests are in multi-agent robotics, reinforcement learning, and diffusion models.
Undergraduate Students
Publications
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QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps.
Cheng Qian*, Yulun Zhang*, Varun Bhatt, Matthew C. Fontaine, Stefanos Nikolaidis, Jiaoyang Li.
Alumni
Yutong received a B.Sc. from Shanghai University in 2020 and an M.Sc. from National University of Singapore in 2021. She is currently a third-year PhD student at National University of Singapore and began visiting Carnegie Mellon University in January 2024. Her research interests include Multi-Agent Reinforcement Learning, Multi-Agent Path Finding and Multi-Robot Coordination.
Publications
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Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding.
(Best Paper on Multi-Robot Systems; Best Student Paper)
He Jiang*, Yutong Wang*, Rishi Veerapaneni, Tanishq Harish Duhan, Guillaume Adrien Sartoretti, Jiaoyang Li. -
LNS2+RL: Combining Multi-agent Reinforcement Learning with Large Neighborhood Search in Multi-agent Path Finding.
(Oral)
Yutong Wang, Tanishq Duhan, Jiaoyang Li, Guillaume Adrien Sartoretti.
Publications
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A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution.
Ying Feng, Adittyo Paul, Zhe Chen, Jiaoyang Li.
Publications
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A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution.
Ying Feng, Adittyo Paul, Zhe Chen, Jiaoyang Li.
Yimin received a B.Eng. in Computer Science from ShanghaiTech University in 2020 and worked as an SDE in Microsoft AzureStack Team for a year. His research is now focused on Multi-Agent Reinforcement Learning and Multi-Agent Path Finding. He also participated in some publications related to grasping and human-computer interaction in ICRA and CHI. He is familiar with traditional data structures and algorithms and has several prizes in NOIP, NOI, and ICPC.
Publications
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Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds.
Yimin Tang, Zhenghong Yu, Jiaoyang Li, Sven Koenig. -
RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding across Different Environments and Tasks.
Yimin Tang*, Xiao Xiong*, Jingyi Xi, Jiaoyang Li, Erdem Bıyık, Sven Koenig. -
ITA-ECBS: A Bounded-Suboptimal Algorithm for The Combined Target-Assignment and Path-Finding Problem.
Yimin Tang, Sven Koenig, Jiaoyang Li. -
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree.
(Best Paper Finalist)
Yimin Tang, Zhongqiang Ren, Jiaoyang Li, Katia Sycara.
Publications
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Efficient Approximate Search for Multi-Objective Multi-Agent Path Finding.
Fangji Wang*, Han Zhang*, Sven Koenig, Jiaoyang Li.
Publications
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Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding.
Hongzhi Zang*, Yulun Zhang*, He Jiang, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li.