arXiv AI
Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence
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MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence
arXiv:2609.25627v1 Announce Type: cross Abstract: General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate prec...
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InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
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WorldLine: Action-Driven Visual Simulation for Robotic Manipulation
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Being-M0.7: A Latent World-Action Model for Humanoid Robots
Being‑M0.7 is a latent world‑action model that learns from a large mixed‑modality human dataset (over 10,000 hours of video‑only, motion‑only, and paired video‑motion data) to predict future latent visual states and motion. The model is pre‑trained on human data, adapted to robot viewpoints during mid‑training, and then combined with an action expert via gated cross‑attention during post‑training to produce executable whole‑body commands. In experiments, Being‑M0.7 outperforms baselines on the SIMPLE benchmark and matches the best baseline on real‑world Unitree G1 loco‑manipulation tasks.
arXiv AI
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World-Ego Modeling for Embodied Video Generation in Long-Horizon Navigation-Manipulation Tasks
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arXiv AI
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Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
arXiv:2607. 03964v1 Announce Type: cross Abstract: World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience.