Magic-W0: A Structured World-Action Foundation Model for Physical Intelligence

arXiv:2609.39870 · cs.RO · Submitted 2026-09-30 · Read on arXiv

cs.RO

Submitted: 2026-09-30

Updated: 2026-10-03

Code: https://github.com/MagiclabRobotics/Magic-W0

Project page: https://embodied.magiclab.top/works/wam/magic-w0/index.html

Terminology

• Generalization — 16× in this paper · explained in 13 episodes
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• World Model — 12× in this paper · explained in 7 episodes
• Foundation Model — 11× in this paper · explained in 3 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Joint Attention — 9× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Latent Representation — 7× in this paper · explained in 2 episodes
• Latent Representations — 7× in this paper · explained in 3 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Latent Space — 5× in this paper · explained in 7 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Latent Dynamics — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Universal Manipulation Interface — 1× in this paper
• VLA Models — 1× in this paper · explained in 3 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Motus: A Unified Latent Action World Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• RT-1: Robotics Transformer for Real-World Control at Scale
• AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
• WorldVLA: Towards Autoregressive Action World Model
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
• Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
• Gemini Robotics: Bringing AI into the Physical World
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• OpenVLA: An Open-Source Vision-Language-Action Model
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
• Causal World Modeling for Robot Control
• SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy
• Unified Video Action Model
• Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
• Depth Anything 3: Recovering the Visual Space from Any Views

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