"world models" Papers
30 papers found
Conference
COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
Yining Shi, Kun Jiang, Qiang Meng et al.
Discrete Codebook World Models for Continuous Control
Aidan Scannell, Mohammadreza Nakhaeinezhadfard, Kalle Kujanpää et al.
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning
Qi Wang, Zhipeng Zhang, Baao Xie et al.
Disentangling Representations through Multi-task Learning
Pantelis Vafidis, Aman Bhargava, Antonio Rangel
DMWM: Dual-Mind World Model with Long-Term Imagination
Lingyi Wang, Rashed Shelim, Walid Saad et al.
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
Guosheng Zhao, Xiaofeng Wang, Zheng Zhu et al.
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
Guosheng Zhao, Chaojun Ni, Xiaofeng Wang et al.
EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling
Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun et al.
ElasticTok: Adaptive Tokenization for Image and Video
Wilson Yan, Volodymyr Mnih, Aleksandra Faust et al.
FOSP: Fine-tuning Offline Safe Policy through World Models
Chenyang Cao, Yucheng Xin, Silang Wu et al.
Imagined Autocurricula
Ahmet Hamdi Güzel, Matthew T Jackson, Jarek Liesen et al.
IRASim: A Fine-Grained World Model for Robot Manipulation
Fangqi Zhu, Hongtao Wu, Song Guo et al.
Learning View-invariant World Models for Visual Robotic Manipulation
Jing-Cheng Pang, Nan Tang, Kaiyuan Li et al.
Learning World Models for Interactive Video Generation
Taiye Chen, Xun Hu, Zihan Ding et al.
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning
Chenjie Hao, Weyl Lu, Yifan Xu et al.
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Yang Tian, Sizhe Yang, Jia Zeng et al.
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Zhenjie Yang, Xiaosong Jia, Qifeng Li et al.
ReSim: Reliable World Simulation for Autonomous Driving
Jiazhi Yang, Kashyap Chitta, Shenyuan Gao et al.
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
Yang Zhang, Xinran Li, Jianing Ye et al.
Simple, Good, Fast: Self-Supervised World Models Free of Baggage
Jan Robine, Marc Höftmann, Stefan Harmeling
ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
Zixun Fang, Kai Zhu, Zhiheng Liu et al.
Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation
Hyungjoo Chae, Namyoung Kim, Kai Ong et al.
World Models Should Prioritize the Unification of Physical and Social Dynamics
Xiaoyuan Zhang, Chengdong Ma, Yizhe Huang et al.
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Yucen Wang, Shenghua Wan, Le Gan et al.
DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
Xiaofeng Wang, Zheng Zhu, Guan Huang et al.
Efficient World Models with Context-Aware Tokenization
Vincent Micheli, Eloi Alonso, François Fleuret
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models
Paul Mattes, Rainer Schlosser, Ralf Herbrich
Learning Latent Dynamic Robust Representations for World Models
Ruixiang Sun, Hongyu Zang, Xin Li et al.
Learning to Model the World With Language
Jessy Lin, Yuqing Du, Olivia Watkins et al.
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
Aditya A. Ramesh, Kenny Young, Louis Kirsch et al.