Hello, I'm Jindou Jia

I am a postdoc in the School of Mechanical and Aerospace Engineering at Nanyang Technological University (NTU), working with Prof. Jianfei Yang at the MARS Lab. I completed my Ph.D. in 2025 from the School of Automation Science and Electrical Engineering and Shenyuan Honors College, Beihang University, China. I was 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 research is learning and predicting uncertainties, aiming at enabling robots to generalize diverse unseen environments.

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CV Research overview

News

  • Nov 2025: 🚀 Started my new position as a Postdoc at NTU！
  • Jul 2025: 🎉 FORESEER has been accepted by The International Journal of Robotics Research！
  • Jun 2025: 😊 Passed my PhD doctoral dissertation defense！
  • Feb 2025: 🎉 Feedback neural network has been selected as an oral paper by 2025 ICLR (top 1.8%)！

Publications

First/co-first author

FORESEER: Recognize and utilize uncertainties by integrating data-based learning and symbolic feedback

FORESEER: Recognize and utilize uncertainties by integrating data-based learning and symbolic feedback

Jindou Jia, Kexin Guo, Yuyang Wang, Sicheng Zhou, Jiayi Zhang, Yuhang Liu, Xiang Yu, Yang Shi, Lei Guo
The International Journal of Robotics Research (IJRR), 2025

We present two converged uncertainty prediction frameworks, enabling accurate prediction of two general kinds of uncertainties, respectively.

Link Project Page Video
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, Lei Guo
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.

Link Project Page PDF arXiv Code
EVOLVER: Online learning and prediction of disturbances for robot control

EVOLVER: Online learning and prediction of disturbances for robot control

Jindou Jia, Wenyu Zhang, Kexin Guo, Jianliang Wang, Xiang Yu, Yang Shi, Lei Guo
IEEE Transactions on Robotics (T-RO), 2024

We present a framework, namely EVOLVER, to mimic the bio-behavior for robotics to achieve rapid transient reaction ability and high precision steady-state performance simultaneously.

Link Project Page Code Video
Accurate high-maneuvering trajectory tracking for quadrotors: A drag utilization method

Accurate high-maneuvering trajectory tracking for quadrotors: A drag utilization method

Jindou Jia, Kexin Guo, Xiang Yu, Weihua Zhao, Lei Guo
IEEE Robotics and Automation Letters (R-AL), 2022

Different from standard approaches that achieve precise tracking by feedforward compensating the estimated drag, this work presents a scheme to appropriately utilize drag.

Link Video
Agile flight control under multiple disturbances for quadrotor: Algorithms and evaluation

Agile flight control under multiple disturbances for quadrotor: Algorithms and evaluation

Jindou Jia, Kexin Guo, Xiang Yu, Lei Guo, Lihua Xie
IEEE Transactions on Aerospace and Electronic Systems (T-AES), 2022

A scheme of anti-disturbance agile flight control is developed for a maneuverable quadrotor unmanned aerial vehicle, subject to the aerodynamic drag, dynamic shift of center of gravity, and motor dynamics.

Link Video
Multiple observers-based anti-disturbance control for a quadrotor UAV against payload and wind disturbance

Multiple observers-based anti-disturbance control for a quadrotor UAV against payload and wind disturbance

Kexin Guo*, Jindou Jia*, Xiang Yu, Lei Guo, Lihua Xie
Control Engineering Practice, 2020

This paper presents a multiple observers-based anti-disturbance control scheme against multiple disturbances for a quadrotor unmanned aerial vehicle.

Link Video
Flight control for quadrotor safety in the presence of CoG shift and loss of motor efficiency

Flight control for quadrotor safety in the presence of CoG shift and loss of motor efficiency

Jindou Jia, Kexin Guo, Chenliang Wang, Ning Shen, Wei Jia, Xiang Yu
International Conference on Unmanned Aircraft Systems (ICUAS), 2022 Oral Presentation

A safety control strategy based on a novel nonlinear disturbance observer and geometric control is developed for a quadrotor unmanned aerial vehicle, subject to the disturbances caused by center-of-gravity shift and loss of motor efficiency.

Link Video
Dual-disturbance observers-based control of UAV subject to internal and external disturbances

Dual-disturbance observers-based control of UAV subject to internal and external disturbances

Kexin Guo*, Jindou Jia*, Xiang Yu, Lei Guo
China Automation Conference, 2019 First Prize of Outstanding Paper

This paper presents an embedded micro loop to enhance the anti-disturbance performance for unmanned aerial vehicles.

Link Video