Pandora: Towards General World Model with Natural Language Actions and Video States

• URL: http://arxiv.org/abs/2406.09455v1
• Date: Wed, 12 Jun 2024 18:55:51 GMT
• Title: Pandora: Towards General World Model with Natural Language Actions and Video States
• Authors: Jiannan Xiang, Guangyi Liu, Yi Gu, Qiyue Gao, Yuting Ning, Yuheng Zha, Zeyu Feng, Tianhua Tao, Shibo Hao, Yemin Shi, Zhengzhong Liu, Eric P. Xing, Zhiting Hu,
• Abstract summary: 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.
• Score: 61.30962762314734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models simulate future states of the world in response to different actions. They facilitate interactive content creation and provides a foundation for grounded, long-horizon reasoning. Current foundation models do not fully meet the capabilities of general world models: large language models (LLMs) are constrained by their reliance on language modality and their limited understanding of the physical world, while video models lack interactive action control over the world simulations. This paper makes a step towards building a general world model by introducing Pandora, 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. Crucially, Pandora bypasses the cost of training-from-scratch by integrating a pretrained LLM (7B) and a pretrained video model, requiring only additional lightweight finetuning. We illustrate extensive outputs by Pandora across diverse domains (indoor/outdoor, natural/urban, human/robot, 2D/3D, etc.). The results indicate great potential of building stronger general world models with larger-scale training.

Related papers

• Vid2World: Crafting Video Diffusion Models to Interactive World Models [38.270098691244314]
  Vid2World is a general approach for leveraging and transferring pre-trained video diffusion models into interactive world models.<n>It performs casualization of a pre-trained video diffusion model by crafting its architecture and training objective to enable autoregressive generation.<n>It introduces a causal action guidance mechanism to enhance action controllability in the resulting interactive world model.
  arXiv  Detail & Related papers  (2025-05-20T13:41:45Z)
• PoE-World: Compositional World Modeling with Products of Programmatic Experts [41.07916209987106]
  Learning how the world works is central to building AI agents that can adapt to complex environments.<n>Recent advances in program synthesis using Large Language Models (LLMs) give an alternate approach which learns world models represented as source code.<n>We show that this approach can learn complex world models from just a few observations. We evaluate the learned world models by embedding them in a model-based planning agent, demonstrating efficient performance and generalization to unseen levels on Atari's Pong and Montezuma's Revenge.
  arXiv  Detail & Related papers  (2025-05-16T03:28:42Z)
• Learning 3D Persistent Embodied World Models [84.40585374179037]
  We introduce a new persistent embodied world model with an explicit memory of previously generated content.<n>During generation time, our video diffusion model predicts RGB-D video of the future observations of the agent.<n>This generation is then aggregated into a persistent 3D map of the environment.
  arXiv  Detail & Related papers  (2025-05-05T17:59:17Z)
• AdaWorld: Learning Adaptable World Models with Latent Actions [76.50869178593733]
  We propose AdaWorld, an innovative world model learning approach that enables efficient adaptation. Key idea is to incorporate action information during the pretraining of world models. We then develop an autoregressive world model that conditions on these latent actions.
  arXiv  Detail & Related papers  (2025-03-24T17:58:15Z)
• 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)
• OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving [12.004183122121042]
  OccLLaMA is an occupancy-language-action generative world model. We build a unified multi-modal vocabulary for vision, language and action. OccLLaMA achieves competitive performance across multiple tasks.
  arXiv  Detail & Related papers  (2024-09-05T06:30:01Z)
• iVideoGPT: Interactive VideoGPTs are Scalable World Models [70.02290687442624]
  World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. This work introduces Interactive VideoGPT, a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations.
  arXiv  Detail & Related papers  (2024-05-24T05:29:12Z)
• 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)
• WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens [75.02160668328425]
  We introduce WorldDreamer, a pioneering world model to foster a comprehensive comprehension of general world physics and motions. WorldDreamer frames world modeling as an unsupervised visual sequence modeling challenge. Our experiments show that WorldDreamer excels in generating videos across different scenarios, including natural scenes and driving environments.
  arXiv  Detail & Related papers  (2024-01-18T14:01:20Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• PaLM-E: An Embodied Multimodal Language Model [101.29116156731762]
  We propose embodied language models to incorporate real-world continuous sensor modalities into language models. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks. Our largest model, PaLM-E-562B with 562B parameters, is a visual-language generalist with state-of-the-art performance on OK-VQA.
  arXiv  Detail & Related papers  (2023-03-06T18:58:06Z)

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.