机器人 / 具身智能

World Models via Policy-Guided Trajectory Diffusion

Marc Rigter, Jun Yamada, Ingmar Posner

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Published in TMLR, March 2024

WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs

Deshun Yang, Luhui Hu, Yu Tian, Zihao Li, Chris Kelly, Bang Yang, Cindy Yang, Yuexian Zou

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.CV

Comments 11 pages, 2 figures, 2 tables

ED2: Environment Dynamics Decomposition World Models for Continuous Control

Jianye Hao, Yifu Yuan, Cong Wang, Zhen Wang

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments 10 pages, 13 figures

Do Transformer World Models Give Better Policy Gradients?

Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Michel Ma and Pierluca D'Oro contributed equally

Slot Structured World Models

Jonathan Collu, Riccardo Majellaro, Aske Plaat, Thomas M. Moerland

专题命中 具身推理 ：world model(title,abstract)；分类 cs.CV、cs.LG

Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving

Daniel Bogdoll, Lukas Bosch, Tim Joseph, Helen Gremmelmaier, Yitian Yang, J. Marius Zöllner

专题命中 具身推理 ：world model(title,abstract)；分类 cs.RO、cs.AI

Comments Accepted for publication at SSCI 2023

Generalization properties of contrastive world models

Kandan Ramakrishnan, R. James Cotton, Xaq Pitkow, Andreas S. Tolias

专题命中 具身推理 ：world model(title,abstract)；分类 cs.CV、cs.LG

Comments Accepted at the NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice

Gradient-based Planning with World Models

Jyothir S, Siddhartha Jalagam, Yann LeCun, Vlad Sobal

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models

Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments NeurIPS 2023

Multi Time Scale World Models

Vaisakh Shaj, Saleh Gholam Zadeh, Ozan Demir, Luiz Ricardo Douat, Gerhard Neumann

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted as spotlight at NeurIPS 2023

ADriver-I: A General World Model for Autonomous Driving

Fan Jia, Weixin Mao, Yingfei Liu, Yucheng Zhao, Yuqing Wen, Chi Zhang, Xiangyu Zhang, Tiancai Wang

专题命中 具身推理 ：world model(title,abstract)；分类 cs.RO、cs.CV

Comments Tech Report

Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT

Dean S. Hazineh, Zechen Zhang, Jeffery Chiu

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Reasoning with Language Model is Planning with World Model

Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, Zhiting Hu

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments EMNLP 2023. Code is available at https://github.com/Ber666/llm-reasoners

UniWorld: Autonomous Driving Pre-training via World Models

Chen Min, Dawei Zhao, Liang Xiao, Yiming Nie, Bin Dai

专题命中 具身推理 ：world model(title,abstract)；分类 cs.RO、cs.CV

Comments 8 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2305.18829

The Effectiveness of World Models for Continual Reinforcement Learning

Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz, Mateusz Żarski, Maciej Wołczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at CoLLAs 2023, 21 pages, 15 figures

Co-Learning Empirical Games and World Models

Max Olan Smith, Michael P. Wellman

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Transformer-based World Models Are Happy With 100k Interactions

Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Published as a conference paper at ICLR 2023. Code is available at https://github.com/jrobine/twm

Neuro-Symbolic World Models for Adapting to Open World Novelty

Jonathan Balloch, Zhiyu Lin, Robert Wright, Xiangyu Peng, Mustafa Hussain, Aarun Srinivas, Julia Kim, Mark O. Riedl

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments 9 pages, 8 figures, Extended Abstract accepted for presentation at AAMAS 2023

Intrinsically Motivated Learning of Causal World Models

Louis Annabi

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments 6 pages, 3 figures, IMOL 2022 workshop

Multi-View Dreaming: Multi-View World Model with Contrastive Learning

Akira Kinose, Masashi Okada, Ryo Okumura, Tadahiro Taniguchi

专题命中 具身推理 ：world model(title)；robotics(abstract)；分类 cs.RO、cs.AI

Comments 7 pages, 8 figures

Mastering Atari with Discrete World Models

Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Published at ICLR 2021. Website: https://danijar.com/dreamerv2

Quantifying Multimodality in World Models

Andreas Sedlmeier, Michael Kölle, Robert Müller, Leo Baudrexel, Claudia Linnhoff-Popien

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Learning Knowledge Graph-based World Models of Textual Environments

Prithviraj Ammanabrolu, Mark O. Riedl

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Camera read, in Proceedings of NeurIPS 2021 Main Conference

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment

Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted @ ICML 2021; Spotlight @ ICLR 2021 "Self-Supervision for Reinforcement Learning Workshop"

Smaller World Models for Reinforcement Learning

Jan Robine, Tobias Uelwer, Stefan Harmeling

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Low-Variance Policy Gradient Estimation with World Models

Michal Nauman, Floris Den Hengst

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Active World Model Learning with Progress Curiosity

Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments ICML 2020. Video of results at https://bit.ly/31vg7v1

Contrastive Learning of Structured World Models

Thomas Kipf, Elise van der Pol, Max Welling

专题命中 具身推理 ：world model(title,abstract)；分类 cs.AI、cs.LG

Comments ICLR 2020

Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models

表征涌现的自举理论：解释不充分性作为表征学习与世界模型的驱动力

Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

机构 * Laboratory of Bioengineering and Nanosciences (LBN), University of Montpellier（生物工程与纳米科学实验室（LBN），蒙彼利埃大学） ； EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès（EuroMov数字健康运动，蒙彼利埃大学，IMT矿山阿尔勒） ； Certified Sophrologist, Sensorimotor Practice, Montpellier, France（认证Sophrologist，运动觉实践，蒙彼利埃，法国） ； Emeritus Professor, University of Montpellier（荣誉教授，蒙彼利埃大学） 纠错

专题命中 具身推理 ：world model(title,abstract)；分类 cs.LG

AI总结 提出表征涌现自举理论（TBER），将解释不充分性视为新表征涌现的积极信号，通过五阶段递归过程驱动表征创新，应用于表征学习、世界模型和科学发现。

Comments 27 pages, 25 references, no figures or tables. Conceptual framework on representational emergence and explanatory insufficiency, with implications for representation learning, world models, autonomous AI, and scientific discovery