Jindou Jia | NTU Postdoc

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

Sep 2026: 🎉 GRIPPER has been accepted by T-RO！
Sep 2026: 🎉 Feedback world model and FLASH have been accepted by 2026 NeurIPS！
Sep 2026: 🎉 ADAPT has been accepted by 2026 CoRL！
Aug 2026: 🎉 PhyFilter has been accepted by Nature npj Robotics！
Aug 2026: 🎉 Our world model survey for robot learning has been accepted by IJRR！
May 2026: ⭐ Released MARS policy, multimodality only when it matters.
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！

Robotic Platforms

Hardware platforms I've worked with over the years.

Publications

All papers below are first, co-first^*, or corresponding^† author work. Full list on Google Scholar →

CoRL'26

ADAPT: Analytical disturbance-aware policy training for humanoid locomotion

Bofan Lyu^*, Jindou Jia^*, Kuangji Zuo, Yanshuo Lu, Shijia Han, Gen Li, Boyu Ma, Jingliang Li, Geng Li, Jianfei Yang

Conference on Robot Learning (CoRL), 2026

We present ADAPT, an analytical disturbance-aware framework for humanoid locomotion that infers joint-level external disturbances from robot dynamics and proprioception alone, without specialized sensors, and feeds them back into policy training for robustness to pushes, payloads, and impacts.

Project Page PDF arXiv

★ Featured

arXiv'26

Δ-Flow matching for robot foundation models

Kuangji Zuo, Tuo An, Xinying Guo, Gen Li, Mengfei Zhao, Jiaqi Bai, Bofan Lyu, Zhuoyuan Yu, Jinghan Yang, Jiayi Li, Jindou Jia^†, Jianfei Yang^†

arXiv, 2026

Scaling A2A to robot foundation models: Δ-flow starts flow matching from recent history instead of Gaussian noise, enabling single-step inference, faster training, and smoother actions.

Project Page PDF arXiv

arXiv'26

MOSS: Mode-oriented source shaping for robot flow matching policies

Zirun Ma^*, Jindou Jia^*, Tuo An, Jiaqi Bai, Jiahua Dong, Jianfei Yang

arXiv, 2026

MOSS makes behavior-mode selection controllable in robot flow matching policies by shaping mode-specific source distributions, separating modes only where the situation is truly multimodal.

Project Page PDF arXiv

arXiv'26

TUCO: Curating simulation demonstrations for sim-to-real robot policy co-training

Ning Zhu, Mengfei Zhao, Yikai Tang, Zhangyujie Sun, Peihao Li, Dongyue Ni, Jindou Jia^†, Jianfei Yang^†

arXiv, 2026

TUCO curates simulation demonstrations for sim-to-real policy co-training, using influence functions to score each demonstration's trajectory utility and set coverage.

PDF arXiv

arXiv'26

APEX: Adaptive policy execution for precise manipulation

Mengfei Zhao, Chenxi Jiang, Tuo An, Jindou Jia^†, Jianfei Yang^†

arXiv, 2026

APEX is a plug-and-play framework that bridges the execution gap of learned manipulation policies by reconstructing dynamically feasible references from policy outputs and adapting them online via state feedback.

Project Page PDF arXiv

★ 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

NeurIPS'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

Conference on Neural Information Processing Systems (NeurIPS), 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%.

Project Page PDF arXiv Code Video

NeurIPS'26

FLASH: Efficient visuomotor policy via sparse sampling

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

Conference on Neural Information Processing Systems (NeurIPS), 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.

Project Page PDF arXiv Code Video

★ Featured

npj Robotics'26

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

npj Robotics (Nature), 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.

Link Project Page PDF arXiv Code

IJRR'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

The International Journal of Robotics Research (IJRR), 2026

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

Link Project Page PDF arXiv Code

★ 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.

Project Page PDF arXiv Video

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

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

★ Featured

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

★ Featured

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

★ Featured

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

★ Featured

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

CEP'20

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

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

CAC'19

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

Talks & Media

Oct 2026 Invited Talk

Classical Control and Planning Meet Modern Learning

Hosted by Prof. Shangke Lyu · Nanjing University (NJU)

Aug 2026 Interview

Robopapers Podcast

Guest interview on A2A policy

Jun 2026 Invited Talk

Seminar on Embodied AI Robotics

Efficient robot policy learning · SIMTech & ARTC, Singapore

Mar 2025 Invited Talk

TechBeat

Invited technical talk on feedback neural ODE

Professional Service

Associate Editor

IEEE Robotics and Automation Letters (RA-L)

Reviewer

T-RO, IJRR, RA-L, ICRA, and other robotics venues