Hello, I'm Meng Wang

I am a Ph.D. student at the School of Automation Science and Electrical Engineering and Shenyuan Honors College, Beihang University, China. I am supervised by Prof. Lei Guo, Prof. Xiang Yu, and Prof. Kexin Guo. Robots deployed in the real world inevitably face uncertainties caused by internal model mismatch and external unmeasured disturbances. The topic of my Ph.D. research is intelligent control and dexterous operation of aerial manipulator.

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Highlights

Publications

Capturing Moving object using onboard sensoring

Capturing Moving object using onboard sensoring

Meng Wang, Jindou Jia, Sicheng Zhou, Kexin Guo, Xiang Yu, Lei Guo
Science Robotics, 2024

This represents the fastest and most accurate vision-based grasping performance reported in the literature.

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Feedback favors the generalization of neural ODEs

Feedback favors the generalization of neural ODEs

Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, jianfei yang, Xiang Yu,
International Conference on Learning Representations (ICLR), 2025 Oral Presentation

We present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement.

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Millimeter-level pick and peg-in-hole task achieved by aerial manipulator

Millimeter-level pick and peg-in-hole task achieved by aerial manipulator

Meng Wang, Zeshuai Chen, Kexin Guo, Xiang Yu, Lei Guo, Wei Wang
IEEE transcations on robotis (IEE TRO), 2024

Building upon the philosophy of disturbance rejection, we propose a predictive optimization scheme that allows aerial manipulator to successfully execute millimeter-level flying pick and peg-in-hole task.

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Precise End-Effector Control for an Aerial Manipulator Under Composite Disturbances: Theory and Experiments

Precise End-Effector Control for an Aerial Manipulator Under Composite Disturbances: Theory and Experiments

Meng Wang, Shangke Lyu, Qianyuan Liu, Ziqi Yang, Kexin Guo, Xiang Yu
IEEE Transactions on Automation Science and Engineering (IEEE TASE), 2024

In this work, a composite control scheme is constructed, which consists of the manipulator joint velocity planner and the dynamic controller. The idea is intuitive. The joint velocity is generated to counteract the fluctuation of the aerial platform. Furthermore, the dynamic controller is developed to accurately track the planned joint velocity in the presence of strong coupling and model uncertainties. The proposed scheme guarantees the stability of the close loop system, making our approach especially promising solution for aerial manipulation under composite disturbances.

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