世界模型 - arXivDaily 专题

1802.10592 2018-10-08 cs.LG cs.AI cs.RO 60%

Model-Ensemble Trust-Region Policy Optimization

Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

专题命中模型式强化学习：model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG、cs.RO

1707.03497 2017-11-08 cs.AI cs.LG 60%

Value Prediction Network

Junhyuk Oh, Satinder Singh, Honglak Lee

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG；dynamics model(abstract)

Comments NIPS 2017

1412.6451 2014-12-22 cs.LG cs.AI cs.RO 60%

Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer

Mark Wernsdorfer, Ute Schmid

专题命中模型式强化学习：model-based RL(abstract)；分类 cs.AI、cs.LG、cs.RO

Comments 14 pages, 4 figures

1304.2024 2014-03-18 cs.LG cs.AI cs.MA stat.ML 60%

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior

Trong Nghia Hoang, Kian Hsiang Low

专题命中模型式强化学习：latent dynamics(abstract)；分类 cs.AI、cs.LG、cs.MA

Comments 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), Extended version with proofs, 10 pages

1711.05008 2017-11-15 cond-mat.mtrl-sci 59%

Cluster dynamics modeling of Mn-Ni-Si precipitates in ferritic-martensitic steel under irradiation

Jia-Hong Ke, Huibin Ke, G. Robert Odette, Dane Morgan

专题命中模型式强化学习：dynamics model(title,abstract)；predictive model(abstract)；predictive models(abstract)

Journal ref Cluster dynamics modeling of Mn-Ni-Si precipitates in ferritic-martensitic steel under irradiation, J. Nucl. Mater. 498 (2018) 83-88

2608.11349 2026-08-13 cs.LG cs.AI 新提交 58%

Dynamics Models for Offline Hyperparameter Selection in Real-World RL

面向真实世界强化学习的离线超参数选择的动力学模型

Jordan Coblin, Han Wang, Martha White, Adam White

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

AI总结本文将校准模型首次应用于市政水处理厂的真实RL场景，评估其在高维非平稳传感器数据上的表现，为离线动力学模型支持真实RL部署提供概念验证。

Comments Accepted to the 2026 Reinforcement Learning Conference (RLC)

2607.20939 2026-08-04 eess.SY cs.AI cs.RO cs.SY 版本更新 58%

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

用于安全可控导管-组织相互作用的相互作用动力学建模与预测控制

Yongyan Cao

机构 * Voryx Robotic LLC

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.RO

AI总结研究安全可控导管-组织相互作用动力学问题，通过建立模型、采用部分物理前馈、预测优化器及增强卡尔曼滤波器等方法，实现无偏移运动调节与接触力安全，模拟结果验证了方法有效性，硬件验证待开展。

2606.23436 2026-06-23 cs.CV cs.AI cs.LG 新提交 58%

Rethinking Object-Centric Representations for Video Dynamics Modeling

重新思考面向对象的视频动态建模表示

Amaury Wei, Ismail Nejjar, Olga Fink

机构 * Intelligent Maintenance and Operation Systems (IMOS) École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院智能维护与操作系统实验室（IMOS））

专题命中模型式强化学习：dynamics model(title)；分类 cs.AI、cs.LG、cs.CV

AI总结提出STAITUS框架，通过解耦每个槽为外观和几何位姿，在运动、遮挡等场景下实现更清晰的分割和更稳定的身份保持。

Comments 17 pages, 6 figures

2603.23977 2026-06-01 cs.LG cs.AI 58%

Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception

电路启发的具有统一神经动力学建模的高阶神经网络用于PDE求解与视觉感知

Tongfei Chen, Jingying Yang, Linlin Yang, Juan Zhang, Jinhu Lü, David Doermann, Chunyu Xie, Long He, Tian Wang, Guodong Guo, Baochang Zhang

机构 * Communication University of China（通信大学）； AI Research, Qihoo 360（360人工智能研究院，奇虎360）； Eastern Institute of Technology, Ningbo（宁波工程技术院）

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

AI总结提出电路启发的高阶神经网络（CHONN），通过基尔霍夫级联组合实现高阶动力学算子，在PDE求解、长期物理预测和ImageNet-1K识别中提升结构保真度和稳定性。

2505.24265 2026-05-01 cs.MA 58%

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning

R3DM：通过动态模型在多智能体强化学习中实现角色发现与多样性

Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep Chinchali

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.MA

AI总结 R3DM通过动态模型最大化智能体角色、观察轨迹与预期未来行为间的互信息，提升多智能体协作效率，实验表明其在SMAC和SMACv2环境中显著提升胜率。

Comments 21 pages, To appear in the International Conference of Machine Learning (ICML 2025)

2603.29315 2026-04-01 cs.RO cs.AI 58%

IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction

IMPASTO：整合基于模型的规划与学习的动力学模型用于机器人油画复现

Yingke Wang, Hao Li, Yifeng Zhu, Hong-Xing Yu, Ken Goldberg, Li Fei-Fei, Jiajun Wu, Yunzhu Li, Ruohan Zhang

机构 * Stanford University（斯坦福大学）； The University of Texas at Austin（德克萨斯大学奥斯汀分校）； University of California, Berkeley（加州大学伯克利分校）； Columbia University（哥伦比亚大学）

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.RO

AI总结 IMPASTO通过整合学习的动力学模型与基于模型的规划，实现机器人基于目标油画图像的复现，无需人类示范或精确模拟，提升复现精度。

2409.19647 2026-03-17 cs.RO cs.AI cs.SY eess.SY 58%

Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation

针对车辆动力学模型估计的混合物理信息神经网络微调

Shiming Fang, Kaiyan Yu

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.RO

AI总结本文提出FTHD方法，结合监督和非监督PINNs，通过微调预训练的DDM，在减少数据量的情况下提升参数估计精度，并通过EKF-FTHD增强数据鲁棒性。

Journal ref Int. J. Intell. Robot. Appl., vol. 9, no. 4, pp. 1594-1610, 2025

2502.08658 2026-01-06 cs.RO cs.AI 58%

Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach

基于知识-数据融合的车辆编队动力学建模与分析：一种物理编码深度学习方法

Hao Lyu, Yanyong Guo, Pan Liu, Shuo Feng, Weilin Ren, Quansheng Yue

机构 * School of Transportation, Southeast University, Nanjing, China, 211189（东南大学交通学院）； Jiangsu Key Laboratory of Urban ITS, Nanjing, China, 210096（江苏省城市智能交通重点实验室）； Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, China, 210096（江苏省现代城市交通技术协同创新中心）； Department of Automation, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China, 100084（自动化系，北京信息科学与技术国家研究中心（BNRist），清华大学）

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.RO

AI总结本文提出PeMTFLN模型，通过物理编码深度学习方法实现车辆编队动力学建模与分析，提升预测精度和稳定性。

Journal ref Information Fusion, Vol. 126, 103622, 2026

2511.21846 2025-12-01 eess.SY cs.AI cs.LG cs.SY 58%

LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models

LILAD: 学习上下文Lyapunov稳定自适应动力学模型

Amit Jena, Na Li, Le Xie

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

AI总结 LILAD通过上下文学习同时学习动态模型和Lyapunov函数，确保系统在分布偏移和任务外情况下的稳定性与适应性。

Comments This article has been accepted for AAAI-26 (The 40th Annual AAAI Conference on Artificial Intelligence)

2510.08556 2025-10-10 cs.RO cs.CV 58%

DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model

Xueyi Liu, He Wang, Li Yi

机构 * Tsinghua University（清华大学）； Peking University（北京大学）； Shanghai Qi Zhi Institute（上海启智研究所）； Galbot Project（Galbot项目）

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.CV、cs.RO

Comments Project Website: https://meowuu7.github.io/DexNDM/ Video: https://youtu.be/tU2Mv8vWftU

2509.23307 2025-09-30 cs.LG cs.AI 58%

A Neural ODE Approach to Aircraft Flight Dynamics Modelling

Gabriel Jarry, Ramon Dalmau, Xavier Olive, Philippe Very

机构 * EUROCONTROL Aviation Sustainability Unit (ASU)（EUROCONTROL航空可持续性单位）； ONERA – DTIS Université de Toulouse（ONERA-DTIS图卢兹大学）

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

2409.18768 2025-02-12 cs.AI cs.LG cs.RO cs.SY eess.SY 58%

Learning from Demonstration with Implicit Nonlinear Dynamics Models

Peter David Fagan, Subramanian Ramamoorthy

专题命中模型式强化学习：dynamics model(title)；分类 cs.AI、cs.LG、cs.RO

Comments 21 pages, 9 figures

2102.11394 2024-10-08 cs.LG cs.RO cs.SY eess.SY stat.ML 58%

Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models

Jan Achterhold, Joerg Stueckler

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.LG、cs.RO

Comments Accepted for publication at the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, with supplementary material. Corrected version (see footnote on p. 6)

2409.02390 2024-09-05 cs.NE cs.AI cs.CV q-bio.NC 58%

Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts

Jie Su, Fang Cai, Shu-Kuo Zhao, Xin-Yi Wang, Tian-Yi Qian, Da-Hui Wang, Bo Hong

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.CV

2309.11148 2024-05-29 cs.RO cs.CV 58%

Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry

Haolong Li, Joerg Stueckler

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.CV、cs.RO

Comments Accepted for publication in IEEE International Conference on Robotics and Automation (ICRA), 2024

2306.11941 2024-05-14 cs.LG cs.AI 58%

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted to ICLR 2024 and EWRL 2023

2403.13850 2024-03-22 cs.LG cs.AI physics.flu-dyn 58%

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

Hao Wu, Fan Xu, Yifan Duan, Ziwei Niu, Weiyan Wang, Gaofeng Lu, Kun Wang, Yuxuan Liang, Yang Wang

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

2305.10912 2023-09-26 cs.AI cs.RO 58%

A Generalist Dynamics Model for Control

Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.RO

2110.14700 2023-08-11 eess.SY cs.SY 58%

DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles

Yongqian Xiao

专题命中模型式强化学习：latent dynamics(abstract)；dynamics model(title)

Journal ref 2023 IEEE International Conference on Robotics and Automation (ICRA)

2306.03405 2023-06-07 cs.RO cs.LG 58%

Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes

Jingyun Ning, Madhur Behl

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.LG、cs.RO

Comments 12 pages, 6 figures, 10 tables

2305.12369 2023-05-23 cs.CV cs.AI cs.LG 58%

HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer

Yubin Kim, Dong Won Lee, Paul Pu Liang, Sharifa Algohwinem, Cynthia Breazeal, Hae Won Park

专题命中模型式强化学习：dynamics model(title)；分类 cs.AI、cs.LG、cs.CV

2303.11756 2023-03-22 cs.RO cs.LG 58%

Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces

Johan Vertens, Nicolai Dorka, Tim Welschehold, Michael Thompson, Wolfram Burgard

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.LG、cs.RO

2110.01894 2023-03-20 cs.LG cs.RO 58%

Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models

Michael Lutter, Jan Peters

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.LG、cs.RO

2202.01889 2022-06-27 cs.LG cs.AI stat.ML 58%

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at ICML 2022

2205.13804 2022-05-30 cs.RO cs.LG 58%

End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control

Moritz Reuss, Niels van Duijkeren, Robert Krug, Philipp Becker, Vaisakh Shaj, Gerhard Neumann

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.LG、cs.RO

Comments Accepted for publication at Robotics: Science and System XVIII (RSS), year 2022. Paper length is 13 pages (i.e. 9 pages of technical content, 1 page of the Bibliography/References and 3 pages of Appendix)

1. 模型式强化学习 1124 篇

Model-Ensemble Trust-Region Policy Optimization

Value Prediction Network

Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior

Cluster dynamics modeling of Mn-Ni-Si precipitates in ferritic-martensitic steel under irradiation

Dynamics Models for Offline Hyperparameter Selection in Real-World RL

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

Rethinking Object-Centric Representations for Video Dynamics Modeling

Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning

IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction

Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation

Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach

LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models

DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model

A Neural ODE Approach to Aircraft Flight Dynamics Modelling

Learning from Demonstration with Implicit Nonlinear Dynamics Models

Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models

Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts

Online Calibration of a Single-Track Ground Vehicle Dynamics Model by Tight Fusion with Visual-Inertial Odometry

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

A Generalist Dynamics Model for Control

DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles

Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes

HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer

Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces

Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models

Generalizing to New Physical Systems via Context-Informed Dynamics Model

End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control