Code
Videos
Welcome to the Multi-Agent Robotic Motion Laboratory!
Message to Prospective Students and Postdocs.
News
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March 01, 2024
Robotics and Automation Letters - Paper Accepted
Our paper focusing on large-scale autonomous robotic exploration of unknown spaces has been accepted for publication in IEEE Robotics and Automation Letters (RA-L). Congratulations Dr. Cao!
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February 23, 2024
IEEE Transactions on Neural Networks and Learning Systems - Paper Accepted
Our paper focusing on neural-based Multi-objective Vehicle Routing Problems (MOVRP) has been accepted for publication in the prestigious IEEE Transactions on Neural Networks and Learning Systems (TNNLS). Congratulations Mingfeng!
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February 16, 2024
Invited Seminar - UPenn GRASP Robotic Seminar (Recording Available)
I was honored to be invited as speaker for one of the regular-series GRASP Robotic seminars, organized and hosted by the great team at the University of Pennsylvania! You can watch or re-watch my invited UPenn GRASP seminar, entitled “Towards Learned Cooperation at Scale in Robotic Multi-Agent Systems,” using the link below:
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January 29, 2024
ICRA 2024 - Three Papers Accepted
Three of our papers have been accepted for presentation at the International Conference on Robotics and Automation (ICRA 2024, which will be held in-person from 13 to 17 May 2024 in Yokohama, JP. One of the papers focuses on learning-based, decentralized multi-robot, multi-task assignment (MRTA), one discusses our new approach to decentralized, learning-based MAPF where agents are able to observe and reason about the entire state of the system through the development and processing of a new informative graph over the domain, and the last one on learning-based ergodic path planning for coverage by a team of heterogeneous robots.
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January 15, 2024
First PhD Student Graduated: CAO Yuhong!
My first PhD student, Mr. Yuhong CAO, has successfully defended his thesis and has been awarded the title of Doctor (PhD) in Mechanical Engineering from NUS! I could not have asked for a better first PhD graduate, congratulations Yuhong!!
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December 21, 2023
Full-Day Tutorial on Ergodic Control accepted at ICRA 2024
My colleagues Profs. Sylvain Calinon, Todd Murphey, Stefan Ivić, Ian Abraham, and Kathleen Fitzsimons, and I have proposed to organize a full-day tutorial on Ergodic control during ICRA 2024, and our proposal has been approved! More details about the schedule, content, and organization of this tutorial will follow via the website we are finalizing, so please stay tuned if you are interested in this very interesting, spectral approach to control, planning, and overall decision-making with a myriad of applications in robotics!
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Current Research Projects
See some of my past projects on my (old) personal webpage.
Decentralized Collision-Free Multi-Agent Path Finding (MAPF)
Distributed reinforcement learning for scalable, decentralized multi-agent path finding in highly-structured environments (e.g., Amazon fulfillment centers)
Communication Learning for True Cooperation in Multi-Agent Systems
Communication learning for simultaneous communication and action policy learning.
Multi-Robot Coordination via Learning-Based Combinatorial Optimization
Reinforcement learning method to solve multi-robot combinatorial optimization problem with specific objective and constraints
Autonomous Robotic Exploration and Search in Complex Environments
Deployment of robots to conduct efficient exploration and search in complex environments
Multi-Agent Search/Surveillance of Adversarial/Evasive Agents
Multi-robot search and monitor an area to locate potentially evasive targets, by learning strategies in a mixed cooperative-competitive environment.
Communication-Aware Multi-Agent Exploration
Decentralised multi-robot exploration in communication-constraint environments, ensuring appropriate connectivity and adaptability in real-world conditions.
Distributed learning based Intelligent Transportation System
Distributed RL for junction-level traffic light phase control, as well as for decentralized CAVs control via communication learning.
Environmental Interactions by Autonomous Legged Robots
The project’s aim is to exploit this manipulative prowess in order to boost the performance of legged robots in both industrial and real-world situations.
People
Faculty
Guillaume SARTORETTI
Postdoctoral Fellows
Yuhong CAO
Staff
Aditya BIDWAI
Swasti KHURANA
PhD Students
Ge SUN
Weiheng DAI
Yutong WANG
Yifeng ZHANG
Yizhuo WANG
Pamela WANG
Chengyang HE
Junkai LU
Derek TAN
Jimmy CHIUN
Joshua Taylor
Master's Students
Maxime DE MONTLEBERT
Peizhuo LI
Yixiao MA
Jingsong LIANG
Mingliang ZHANG
Ningze ZHONG
Mingxiang DENG
Dixiao NIU
Shuheng WANG
Sichang SU
Yurou LI