Hello, I'm Jindou Jia

I am a postdoc in the MARS Lab at Nanyang Technological University (NTU), working with Prof. Jianfei Yang. 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. My research aims at generalizable robot learning. By integrating world models, generative policies, and uncertainty prediction with physical priors, I work toward robots that reliably transfer from training to unseen environments.

Scholar Github ResearchGate CV Research

Research Interests

Robot learning World model Generative policy Uncertainty prediction
๐ŸšDrone ๐Ÿ•Quadruped robot ๐ŸฆพRobotic arm ๐Ÿค–Humanoid robot

News

  • May 2026: ๐Ÿ“š Released our World Model survey for robot learning.
  • Apr 2026: ๐ŸŽ‰ A2A policy have been accepted by 2026 RSS๏ผ
  • Feb 2026: ๐ŸŽ‰ Two papers have been accepted by 2026 ICRA๏ผ
  • Nov 2025: ๐Ÿš€ Started my new position as a Postdoc at NTU๏ผ
  • Jul 2025: ๐ŸŽ‰ FORESEER has been accepted by IJRR๏ผ
  • Jun 2025: ๐Ÿ˜Š Passed my PhD doctoral dissertation defense๏ผ
  • Feb 2025: ๐ŸŽ‰ Feedback neural network has been selected as an oral paper by 2025 ICLR (top 1.8%)๏ผ
  • Jul 2024: ๐ŸŽ‰ Our paper, TRACE, won the Best Student Paper Award at IEEE ICCA 2024!
  • Oct 2023: ๐ŸŽ‰ EVOLVER has been accepted by T-RO๏ผ

Publications

First/co-first/corresponding author

Preprint

Preprint
Physics filtering favors the generalization of robot learning

Physics filtering favors the generalization of robot learning

Jindou Jia*, Shixuan Han*, Meng Wang*, Gen Li, Zihan Yang, Sicheng Zhou, Kexin Guoโ€ , Jianfei Yangโ€ , Xiang Yu, Wei Wang, Lei Guo
Preprint, 2026

We propose PhyFilter, which enhances both generalization and interpretability of robot learning. PhyFilter operates by correcting learning outcomes according to readily accessible physical differential structure and real-time state feedback.

Project Page PDF Code
arXiv'26
World model for robot learning: A comprehensive survey

World model for robot learning: A comprehensive survey

Bohan Hou*, Gen Li*, Jindou Jia*, Tuo An*, Xinying Guo*, Sicong Leng, Haoran Geng, Yanjie Ze, Tatsuya Harada, Philip Torr, Oier Mees, Marc Pollefeys, Zhuang Liu, Jiajun Wu, Pieter Abbeel, Jitendra Malik, Yilun Du, Jianfei Yang
arXiv, 2026

A comprehensive survey of world models for robot learning, covering world models as policies and as simulators across recent literature.

Project Page PDF arXiv Code
arXiv'26
Optimizing control-friendly trajectories with self-supervised residual learning

Optimizing control-friendly trajectories with self-supervised residual learning

Kexin Guo, Zihan Yang, Yuhang Liu, Jindou Jiaโ€ , Xiang Yu
arXiv, 2026

The presented trajectory optimizer outputs trajectories that are friendly to the following control level.

PDF arXiv

2026

RSS'26
Action-to-action flow matching

Action-to-action flow matching

Jindou Jia*, Gen Li*, Xiangyu Chen, Tuo An, Yuxuan Hu, Jingliang Li, Xinying Guo, Jianfei Yang
Robotics: Science and Systems (RSS), 2026

We introduces A2A, an efficient generative paradigm that replaces noise-based initialization with action-to-action transport.

Project Page PDF arXiv Video
ICRA'26
Learning-based observer for coupled disturbance

Learning-based observer for coupled disturbance

Jindou Jia*, Meng Wang*, Zihan Yang, Bin Yang, Yuhang Liu, Kexin Guo, Xiang Yu
IEEE International Conference on Robotics and Automation (ICRA), 2026

We present a learning-based observer, enabling accurate prediction of the coupled disturbance consisting of internal uncertainties and external disturbances.

PDF arXiv

2025

IJRR'25
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
ICLR'25
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

2024

T-RO'24
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

2022

R-AL'22
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
T-AES'22
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
ICUAS'22
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

2020

CEP'20
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

2019

CAC'19
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