Xuning Yang @xuningy
Senior Research Scientist @ NVIDIA | PhD in Robotics @ CMU Seattle, WA Joined March 2009-
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The next wave of robot foundation models is here. ⚡ World-Action Models are emerging as a powerful new approach for physical AI, combining pretrained scene dynamics with action generation. Learn more ➡️ nvda.ws/43C1abm
A fantastic technical deep dive into what's happening now and next in robot foundation models from @moritz_reuss. We're thinking deeply about *how* to build an efficient task generalist policy. Check it out!
World-Action Models (WAMs) have become the second dominant recipe for robot foundation models, next to classical VLAs. So where do they come from, and how do they compare vs VLAs? I wrote an small overview of the WAM landscape, with some personal takes: developer.nvidia.com/blog/pretraine…
VoLo is a really good way to address long horizon manipulation problems is to combine a variety of tools and capabilities in robotics into a single system. We call this a physical orchestrator. We've come a long way since code-as-policies.
Wonderful to be back from #CVPR2026, and excited to share the release of our follow-up work: VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation VoLo introduces the idea of a physical orchestrator for open-vocabulary, long-horizon manipulation. Our goal
@chris_j_paxton We built RoboLab to answer the question "How should we *objectively* evaluate *real-world* generalist policies at scale?" which is exactly where we differentiate. The noise, though, is a problem of statistics and quantifying the sim2real gap. Unfortunately this is not solved.
Cosmos3 (post-trained on DROID) surpassed strong VLA & WAM baselines to rank #1 on RoboLab All the compute FLOPs invested during the massive Cosmos3 pre-training and mid-training contribute to unlocking a better robot foundation model.😄
🎉 We added 2 SOTA WAMs to the RoboLab Leaderboard 🎉 Current leaders on RoboLab-120 (specific instr.): 🥇Cosmos3-Nano-Policy (39.7%) 🥈π0.5 (28.1%) 🥉DreamZero (28.1%) → See full results at: research.nvidia.com/labs/srl/proje… → All policy clients available at: github.com/NVlabs/RoboLab/
🎉 We added 2 SOTA WAMs to the RoboLab Leaderboard 🎉 Current leaders on RoboLab-120 (specific instr.): 🥇Cosmos3-Nano-Policy (39.7%) 🥈π0.5 (28.1%) 🥉DreamZero (28.1%) → See full results at: research.nvidia.com/labs/srl/proje… → All policy clients available at: github.com/NVlabs/RoboLab/
@wang_siyin Results coming soon, will be updated on the website
When every generalist robot model scores 95%+ on a benchmark, the numbers become meaningless. What if we built a photorealistic benchmark that never saturates and can generate new scenes and tasks with AI Workflows in minutes? We introduce RoboLab! 🧵(1/6)
a new form of greeting has dropped: see you at icml🇰🇷 1/1 accepted to ICML. more details soon
We are still far from zero-shot policy deployment on new tasks
Evaluation is a critical bottleneck in building robot foundation models. Check out our latest work RoboLab, led by @xuningy, which addresses this exact challenge. Its a high-fidelity simulation environment for testing these models. A truly generalist policy should be able to
@YuXiang_IRVL Far from it, but the field is moving fast. We have results from DreamZero coming very soon.
Generalist robot policies need a benchmark that works across any robot and any policy. 🦾 Introducing RoboLab, a high‑fidelity simulation benchmark built on NVIDIA Isaac and Omniverse to evaluate generalist robot policies in diverse, photoreal, physics‑based environments. Coming soon to the NVIDIA Isaac Lab‑Arena roadmap for large‑scale, robotic policy evaluation. 📖 nvda.ws/47RbOgX #NationalRoboticsWeek
RoboLab comes with RoboLab-120 — a curated, diverse benchmark of 120 tasks to get started. Set up and run in <20 min. (6/6) Try it out 👇 🌐 research.nvidia.com/labs/srl/proje… 📄 arxiv.org/abs/2604.09860 💻 github.com/NVLabs/RoboLab
→ Customization: Comes with 200+ objects, 100+ backgrounds, lighting, camera poses… don’t like it? No problem, add your own → Diagnostics: motion quality, failure events, + sensitivity analysis for failure attribution (5/6)
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