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World generation is a bottleneck for robotics. We’re exploring how generative 3D worlds can reduce manual simulation setup and enable broader, more realistic evaluation 🧵

World Labs

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148,956 次观看 • 5 个月前 •via X (Twitter)

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Researchers are using Marble to generate simulation-ready robotics environments (scenes + collider meshes) then bring them into NVIDIA Robotics Isaac Sim for training + evaluation without any manual environment setup. Case Study:

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We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

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SAM 3D enables accurate 3D reconstruction from a single image, supporting real-world applications in editing, robotics, and interactive scene generation. Matt, a SAM 3D researcher, explains how the two-model design makes this possible for both people and complex environments. 🔗 Read the SAM 3D Objects research paper: 🔗 Read the SAM 3D Body research paper:

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AgentHub (AgentHub) is the missing layer in the AI agent stack: simulation and evaluation for complex agents. Agents today use tools, APIs, browsers, file systems, and more - AgentHub builds realistic sandbox environments and the infra to easily test them.

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Y Combinator

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Punchline: distill world models from simulation to enable fast, stable real-world robot adaptation. Simulation is nearly always wrong. But in Simulation Distillation, we ask a simple question: How do we perform simulation pretraining such that real-world adaptation becomes trivially easy? Let's take a closer look (1/n)

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32,411 次观看 • 1 个月前