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 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.

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News

May 2026: ⭐ Released MARS policy, multimodality only when it matters.
May 2026: 📚 Released our World Model survey for robot learning.
Apr 2026: 🎉 A2A policy have been accepted by 2026 RSS！
Feb 2026: 🎉 Two papers have been accepted by 2026 ICRA！
Nov 2025: 🚀 Started my new position as a Postdoc at NTU！
Jul 2025: 🎉 FORESEER has been accepted by IJRR！
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%)！
Jul 2024: 🎉 Our paper, TRACE, won the Best Student Paper Award at IEEE ICCA 2024!
Oct 2023: 🎉 EVOLVER has been accepted by T-RO！

Publications

Showing first, co-first^*, or corresponding^† author papers only. Full list on Google Scholar →

Preprint

★ Featured

arXiv'26

MARS policy: Multimodality only when it matters

Jindou Jia^*, Tuo An^*, Yuxuan Hu^*, Gen Li, Jingliang Li, Bohan Hou, Xiangyu Chen, Jiaqi Bai, Bofan Lyu, Jianfei Yang

arXiv, 2026

We present MARS, a generative policy that adaptively invokes stochasticity only when behavioral branching truly matters and reverts to deterministic learning otherwise, yielding 16.67% higher success and 83.20% lower inference latency in real-world tests.

Project Page PDF arXiv Video

arXiv'26

Feedback world model enables precise guidance of diffusion policy

Tuo An^*, Jindou Jia^*, Gen Li, Jingliang Li, Chuhao Zhou, Pengfei Liu, Bofan Lyu, Jiaqi Bai, Xinying Guo, Geng Li, Jianfei Yang

arXiv, 2026

We close the prediction–observation loop in world models with a lightweight feedback state that corrects future predictions online, paired with action-aware guidance to emphasize controllable dimensions, reducing prediction error by up to 76.4% and improving OOD success by 30%.

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arXiv'26

FLASH: Efficient visuomotor policy via sparse sampling

Jiaqi Bai^*, Jindou Jia^*, Yuxuan Hu, Gen Li, Xiangyu Chen, Tuo An, Kuangji Zuo, Jianfei Yang

arXiv, 2026

We present FLASH, an efficient visuomotor policy that replaces iterative denoising with continuous Legendre polynomial trajectories, enabling single-step inference for real-time robot control.

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Preprint

Physics filtering favors the generalization of robot learning

Jindou Jia^*, Shixuan Han^*, Meng Wang^*, Gen Li, Zihan Yang, Sicheng Zhou, Kexin Guo^†, Jianfei Yang^†, 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

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.

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arXiv'26

Optimizing control-friendly trajectories with self-supervised residual learning

Kexin Guo, Zihan Yang, Yuhang Liu, Jindou Jia^†, Xiang Yu

arXiv, 2026

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

PDF arXiv

2026

★ Featured

RSS'26

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.

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ICRA'26

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

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

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

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

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

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

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