Cosmos World Foundation Model Platform for Physical AI
- URL: http://arxiv.org/abs/2501.03575v1
- Date: Tue, 07 Jan 2025 06:55:50 GMT
- Title: Cosmos World Foundation Model Platform for Physical AI
- Authors: NVIDIA, :, Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, Daniel Dworakowski, Jiaojiao Fan, Michele Fenzi, Francesco Ferroni, Sanja Fidler, Dieter Fox, Songwei Ge, Yunhao Ge, Jinwei Gu, Siddharth Gururani, Ethan He, Jiahui Huang, Jacob Huffman, Pooya Jannaty, Jingyi Jin, Seung Wook Kim, Gergely Klár, Grace Lam, Shiyi Lan, Laura Leal-Taixe, Anqi Li, Zhaoshuo Li, Chen-Hsuan Lin, Tsung-Yi Lin, Huan Ling, Ming-Yu Liu, Xian Liu, Alice Luo, Qianli Ma, Hanzi Mao, Kaichun Mo, Arsalan Mousavian, Seungjun Nah, Sriharsha Niverty, David Page, Despoina Paschalidou, Zeeshan Patel, Lindsey Pavao, Morteza Ramezanali, Fitsum Reda, Xiaowei Ren, Vasanth Rao Naik Sabavat, Ed Schmerling, Stella Shi, Bartosz Stefaniak, Shitao Tang, Lyne Tchapmi, Przemek Tredak, Wei-Cheng Tseng, Jibin Varghese, Hao Wang, Haoxiang Wang, Heng Wang, Ting-Chun Wang, Fangyin Wei, Xinyue Wei, Jay Zhangjie Wu, Jiashu Xu, Wei Yang, Lin Yen-Chen, Xiaohui Zeng, Yu Zeng, Jing Zhang, Qinsheng Zhang, Yuxuan Zhang, Qingqing Zhao, Artur Zolkowski,
- Abstract summary: Physical AI needs a digital twin of itself, the policy model, and a digital twin of the world, the world model.
We present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups.
- Score: 136.1002343616157
- License:
- Abstract: Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make our platform open-source and our models open-weight with permissive licenses available via https://github.com/NVIDIA/Cosmos.
Related papers
- DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model [65.43473733967038]
We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics.
Our dataset features video clips with a complete set of driving maneuvers, diverse multi-agent interplay, and rich open-world driving knowledge.
arXiv Detail & Related papers (2024-10-14T17:19:23Z) - Recording First-person Experiences to Build a New Type of Foundation Model [0.0]
We have developed a recording rig that captures what the wearer is seeing and hearing as well as their skin conductance.
AI algorithms are used to process this data into a rich picture of the environment and internal states of the subject.
This type of model has many potential applications, including recommendation, personal assistance, GAN systems, dating and recruitment.
arXiv Detail & Related papers (2024-07-31T11:51:26Z) - A New Type of Foundation Model Based on Recordings of People's Emotions and Physiology [0.0]
A first-person foundation model would map environmental stimuli to a person's emotional and physiological states.
We have developed a recording rig that captures what the wearer is seeing and hearing as well as their emotional and physiological states.
This novel source of data could help to address the shortage of new data for building the next generation of foundation models.
arXiv Detail & Related papers (2024-07-31T11:14:45Z) - Apple Intelligence Foundation Language Models [109.60033785567484]
This report describes the model architecture, the data used to train the model, the training process, and the evaluation results.
We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
arXiv Detail & Related papers (2024-07-29T18:38:49Z) - Pandora: Towards General World Model with Natural Language Actions and Video States [61.30962762314734]
Pandora is a hybrid autoregressive-diffusion model that simulates world states by generating videos and allows real-time control with free-text actions.
Pandora achieves domain generality, video consistency, and controllability through large-scale pretraining and instruction tuning.
arXiv Detail & Related papers (2024-06-12T18:55:51Z) - Diffusion for World Modeling: Visual Details Matter in Atari [22.915802013352465]
We introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model.
We analyze the key design choices that are required to make diffusion suitable for world modeling, and demonstrate how improved visual details can lead to improved agent performance.
DIAMOND achieves a mean human normalized score of 1.46 on the competitive Atari 100k benchmark; a new best for agents trained entirely within a world model.
arXiv Detail & Related papers (2024-05-20T22:51:05Z) - Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond [101.15395503285804]
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI)
In this survey, we embark on a comprehensive exploration of the latest advancements in world models.
We examine challenges and limitations of world models, and discuss their potential future directions.
arXiv Detail & Related papers (2024-05-06T14:37:07Z) - Mastering Atari with Discrete World Models [61.7688353335468]
We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model.
DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model.
arXiv Detail & Related papers (2020-10-05T17:52:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.