arXiv Computer Vision
OSWAM: An Effective World Action Model with One-Step Action Generation
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arXiv AI
Aug 13
Foresight Without Seeing: Latent Futures for World Action Models
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
arXiv AI
Oct 1
Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination
arXiv:2609.38984v1 Announce Type: cross Abstract: World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and...
arXiv Computer Vision
3d ago
DriveUniTok: A Unified Multimodal Tokenizer for Driving World-Action Models
arXiv:2610.35044v1 Announce Type: new Abstract: World models are emerging as a fundamental component of policy learning for real-world agents, enabling agents to predict future world states and plan...
arXiv Computer Vision
Oct 23
Self-Distilling Future Video Representations for World Action Models
The paper introduces FRED, a framework that self‑distills future video representations for World Action Models (WAMs) by predicting these representations from noise using ground‑truth future observations as targets. By conditioning on historical observations, FRED improves the quality of future representations obtained in a single denoising step, thereby enhancing action prediction efficiency. Experiments on real‑world manipulation tasks show that FRED outperforms existing WAMs, especially on long‑horizon, out‑of‑domain, and zero‑shot scenarios, achieving a 50.0% success rate on tasks only seen in human videos versus 19.4% for the best competitor.
arXiv AI
Aug 10
Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models
arXiv:2608. 06994v1 Announce Type: cross Abstract: World Action Models (WAMs) aim to construct a unified architecture capable of understanding world state evolution and guiding to generative motion planning.
arXiv AI
Oct 21
De-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control
arXiv:2610.28122v1 Announce Type: cross Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pre...