Meng Guo
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Meng Guo (国萌) |
Research Interest
My general research interest is task and motion planning for robots, particularly in two domains: (1) distributed and hybrid control of interconnected multi-robot systems under complex goals; (2) skill learning and planning of robotic manipulators.
It is an interdisciplinary area between continuous motion control and discrete planning, under various constraints such as timing or resource limits and geometric or dynamic properties. Currently, I investigate how to combine data-driven methods with model-based formulations to improve data efficiency and online performance.
See my publications, Google Scholar profile, and GitHub repositories.
Openings
Prospective PhD students, postdoctoral researchers, undergraduate researchers, and summer interns interested in robotics and multi-agent systems are welcome to join us at the School of Advanced Manufacturing and Robotics, Peking University. Please include a CV and a brief description of your research interests when contacting me.
News
Aug. 15: Our RA-L paper FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots was accepted. [BibTeX] [PDF] [Video]
July 21: Our TASE paper AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration was accepted. [BibTeX] [PDF] [Video]
June: We presented seven papers at ICRA 2026 and contributed to the RoboARCH, CR2, Planetary Robotics, and SCI-MARS workshops.
April 12: Our TASE paper HECTOR: Human-centric Hierarchical Coordination and Supervision of Robotic Fleets under Continual Temporal Tasks was accepted. [BibTeX] [PDF] [Video]
Jan. 1: Four papers were accepted for ICRA 2026:
BodyGuards: Escorting by Multiple Robots in Unknown Environment under Limited Communication. [BibTeX] [PDF] [Video]
CLOT: Multi-robot Motion Planning via Collaborative Optimal Transport under Signal Temporal Logic Tasks. [BibTeX] [PDF] [Video]
UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks. [BibTeX] [PDF]
Beyond Pairwise Costs: Hyper Graph of Convex Sets for Smoothness-Aware Trajectory Planning. [BibTeX] [PDF]
Jan. 1: Our RA-L paper CoCoPlan: Adaptive Coordination and Communication for Multi-robot Systems in Dynamic and Unknown Environments was published. [BibTeX] [PDF] [Video]