DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge

• URL: http://arxiv.org/abs/2507.04447v2
• Date: Thu, 17 Jul 2025 06:03:12 GMT
• Title: DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
• Authors: Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunnan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, He Wang, Zhizheng Zhang, Li Yi, Wenjun Zeng, Xin Jin,
• Abstract summary: We propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling.<n>DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning.<n>Experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks.
• Score: 56.3802428957899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited to challenging image-based forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information. To address these limitations, we propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling, thereby establishing a perception-prediction-action loop for manipulation tasks. Specifically, DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning. This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting. To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action representations from shared latent features. Extensive experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.

Related papers

• WorldVLA: Towards Autoregressive Action World Model [43.74612972649639]
  We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation.<n>WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework.
  arXiv  Detail & Related papers  (2025-06-26T17:55:40Z)
• LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation [51.834607121538724]
  We propose LaDi-WM, a world model that predicts the latent space of future states using diffusion modeling.<n>We show that LaDi-WM significantly enhances policy performance by 27.9% on the LIBERO-LONG benchmark and 20% on the real-world scenario.
  arXiv  Detail & Related papers  (2025-05-13T04:42:14Z)
• HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model [54.64088247291416]
  A fundamental objective of manipulation policy design is to endow robots to comprehend human instructions, reason about scene cues, and execute generalized actions in dynamic environments.<n>Recent autoregressive vision-language-action (VLA) methods inherit common-sense reasoning capabilities from vision-language models (VLMs) for next action-token prediction.<n>We introduce HybridVLA, a unified framework that absorbs the continuous nature of diffusion-based actions and the contextual reasoning of autoregression.
  arXiv  Detail & Related papers  (2025-03-13T17:59:52Z)
• Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration [28.825612240280822]
  We propose a novel framework that integrates language understanding, egocentric scene perception, and motion control, enabling universal humanoid control.<n>Humanoid-VLA begins with language-motion pre-alignment using non-egocentric human motion datasets paired with textual descriptions.<n>We then incorporate egocentric visual context through a parameter efficient video-conditioned fine-tuning, enabling context-aware motion generation.
  arXiv  Detail & Related papers  (2025-02-20T18:17:11Z)
• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model [45.03115608632622]
  spatial understanding is the keypoint in robot manipulation.<n>We propose SpatialVLA to explore effective spatial representations for the robot foundation model.<n>We show the proposed Adaptive Action Grids offer a new and effective way to fine-tune the pre-trained SpatialVLA model for new simulation and real-world setups.
  arXiv  Detail & Related papers  (2025-01-27T07:34:33Z)
• 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)
• 3D-VLA: A 3D Vision-Language-Action Generative World Model [68.0388311799959]
  Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. We propose 3D-VLA by introducing a new family of embodied foundation models that seamlessly link 3D perception, reasoning, and action. Our experiments on held-in datasets demonstrate that 3D-VLA significantly improves the reasoning, multimodal generation, and planning capabilities in embodied environments.
  arXiv  Detail & Related papers  (2024-03-14T17:58:41Z)
• DREAMWALKER: Mental Planning for Continuous Vision-Language Navigation [107.5934592892763]
  We propose DREAMWALKER -- a world model based VLN-CE agent. The world model is built to summarize the visual, topological, and dynamic properties of the complicated continuous environment. It can simulate and evaluate possible plans entirely in such internal abstract world, before executing costly actions.
  arXiv  Detail & Related papers  (2023-08-14T23:45:01Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)

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.