世界模型

Learning active tactile perception through belief-space control

Jean-François Tremblay, David Meger, Francois Hogan, Gregory Dudek

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.RO

Comments 10 pages + references, 6 figures

Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking

David Rapado Rincon, Eldert J. van Henten, Gert Kootstra

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.CV、cs.RO

Thinker: Learning to Plan and Act

Stephen Chung, Ivan Anokhin, David Krueger

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 38 pages

Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network

Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob Foerster

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring

Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments Keywords: Mistrust Scores, Latent-Space, Model monitoring, Trustworthy AI, Explainable AI, Semantic-guided AI

Journal ref IEEE Transactions on Artificial Intelligence, 2023

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents

Rafael Rodriguez-Sanchez, Benjamin A. Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Model-Based Simulation for Optimising Smart Reply

Benjamin Towle, Ke Zhou

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments This paper has been accepted to appear at ACL 2023

Developmental Curiosity and Social Interaction in Virtual Agents

Chris Doyle, Sarah Shader, Michelle Lau, Megumi Sano, Daniel L. K. Yamins, Nick Haber

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 6 pages, 5 figures, 2 tables; accepted to CogSci 2023 with full paper publication in the proceedings

Model Predictive Control with Self-supervised Representation Learning

Jonas Matthies, Muhammad Burhan Hafez, Mostafa Kotb, Stefan Wermter

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Persistent Nature: A Generative Model of Unbounded 3D Worlds

Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG、cs.CV

Comments CVPR camera ready version, project page: https://chail.github.io/persistent-nature/

Learning State Representations via Retracing in Reinforcement Learning

Changmin Yu, Dong Li, Jianye Hao, Jun Wang, Neil Burgess

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG；dynamics model(abstract)

Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts

Po-Hsiang Chiu, Manfred Huber

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 37 pages, 15 figures

Modelling non-reinforced preferences using selective attention

Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 4 pages, 3 figures - Workshop Track: 1st Conference on Lifelong Learning Agents, 2022

Continual Predictive Learning from Videos

Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG、cs.CV

Comments Accepted by CVPR 2022 (Oral). Code is available at https://github.com/jc043/CPL

Dream to Explore: Adaptive Simulations for Autonomous Systems

Zahra Sheikhbahaee, Dongshu Luo, Blake VanBerlo, S. Alex Yun, Adam Safron, Jesse Hoey

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Reinforced Imitation Learning by Free Energy Principle

Ryoya Ogishima, Izumi Karino, Yasuo Kuniyoshi

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Sequential Topological Representations for Predictive Models of Deformable Objects

Rika Antonova, Anastasiia Varava, Peiyang Shi, J. Frederico Carvalho, Danica Kragic

专题命中 通用世界模型 ：predictive model(title,abstract)；predictive models(title,abstract)；分类 cs.LG、cs.CV、cs.RO

Comments To appear in PMLR (Proceedings of Machine Learning Research) as part of L4DC (Learning for Dynamics and Control) conference proceedings

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs

Daniel P. Sawyer, Miguel Lázaro-Gredilla, Dileep George

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.RO

Comments 7 pages, 4 figures, 5 tables, to be presented at CogSci 2020

Human-like general language processing

Feng Qi, Guanjun Jiang

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Structural Pruning in Deep Neural Networks: A Small-World Approach

Gokul Krishnan, Xiaocong Du, Yu Cao

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG、cs.CV

Multi-view Clustering with the Cooperation of Visible and Hidden Views

Zhaohong Deng, Ruixiu Liu, Te Zhang, Peng Xu, Kup-Sze Choi, Bin Qin, Shitong Wang

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments This paper has been submitted to IEEE TKDE in Jun. 2019

Inverse Reinforcement Learning from Summary Data

Antti Kangasrääsiö, Samuel Kaski

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments To appear in ECMLPKDD'2018

A Base Camp for Scaling AI

C. J. C. Burges, T. Hart, Z. Yang, S. Cucerzan, R. W. White, A. Pastusiak, J. Lewis

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling

Arjun Somayazulu, Sagnik Majumder, Changan Chen, Kristen Grauman

专题命中 通用世界模型 ：environment model(title)；分类 cs.CV、cs.RO

Comments Project page: https://vision.cs.utexas.edu/projects/active_rir/

Smart Home Environment Modelled with a Multi-Agent System

Mohammad Rasras, Iuliana Marin, Serban Radu

专题命中 通用世界模型 ：environment model(title)；分类 cs.AI、cs.MA

Comments 12 pages, 8 figures, journal article

Journal ref U.P.B. Sci. Bull., Series C, Vol. 85, Iss. 1, 2023, ISSN 2286-3540

Shaping Belief States with Generative Environment Models for RL

Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aaron van den Oord

专题命中 通用世界模型 ：environment model(title)；分类 cs.AI、cs.LG

Comments pre-print

Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds

面向持续故事与交互世界的长时序视听生成

Nan Duan, Haoyang Huang, Weiyang Jin, Haoran Li, Yaowei Li, Yuming Li, Yijun Liu, Xin Lu, Xiaoxiao Ma, Yanwen Ma, Yaofeng Su, Yilang Sun, Haoyu Wang, Zeyue Xue, Songchun Zhang, Junhao Zhuang

机构 * Joy Future Academy, JD（京东探索研究院） 纠错

专题命中 通用世界模型 ：world-model(abstract)；world-model(abstract)；分类 cs.CV

AI总结 研究针对长时序视听生成的需求，提出JoyAI-Echo-1.5系统，含长视频与世界模型变体，通过专用技术实现跨镜头一致性等性能，在相关基准上取得领先结果，为生成连贯内容提供基础。

Comments Project page: this https URL (https://echo-team-joy-future-academy-jd.github.io/Echo-1.5-Page/)

Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Framework

追踪未标记的风暴：拉格朗日大气JEPA框架中的跨变量迁移

K M Anirudh, S Sandeep, Hariprasad Kodamana

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG

AI总结 该研究提出跨变量代理学习方法，基于M-JEPA在无降水监督下预训练，实现了优于ECMWF集合的季风降水预测，为大气表示迁移提供诊断框架。

Comments 8 pages, 3 figures, plus supplementary material

$ϕ$-Scene: Physically Grounded Image-to-3D Scene Reconstruction

$\phi$-Scene: 物理驱动的图像到3D场景重建

Haodong Li, Lulu Shao, Haolin Lu, Yu Fu, Yen-Ru Chen, Seemandhar Jain, Manmohan Chandraker

机构 * University of California San Diego（加州大学圣地亚哥分校） 纠错

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.CV

AI总结 提出$\phi$-Scene方法，将单图像3D场景重建视为拓扑驱动的物理组装过程，通过SDF优化和刚体仿真解决穿透与不稳定接触问题，在3D-Front数据集上取得最优性能。

Comments Project page: this https URL (https://phi-scene.github.io/)

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

通用干预下的自动、去偏和不变反事实生成

Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina

机构 * Cornell Tech（康奈尔科技） ； Cornell University（康奈尔大学） ； Department of Biostatistics, Department of Population Health（生物统计学系、人口健康系） ； New York University Grossman School of Medicine（纽约大学格罗斯曼医学院） 纠错

专题命中 通用世界模型 ：world model(abstract)；world model(abstract)；分类 cs.LG

AI总结 提出ADIGen框架，结合Riesz回归、因果不变性和正交统计学习，实现通用干预下反事实生成的自动、去偏和不变性，并提供过剩风险界。