{"author":{"name":"Katie Washabaugh","slug":"katie-washabaugh","article_count":2,"latest_published_at":"2026-05-28T16:05:33.313+00:00","profile_url":"https://vff.ai/authors/katie-washabaugh","api_url":"https://vff.ai/api/authors/katie-washabaugh"},"articles":[{"slug":"nvidia-research-advances-robotics-from-simulation-to-the-real-world","title":"NVIDIA's Simulation-to-Real Robotics Reach 80% Success in Live Environments","url":"https://vff.ai/article/2026/05/28/nvidia-research-advances-robotics-from-simulation-to-the-real-world","content_type":"aggregated_news","summary":"NVIDIA Research presented eight papers at ICRA demonstrating how simulation-to-real transfer is enabling robots to operate reliably in dynamic, unpredictable environments. The work spans multi-arm coordination, cross-embodiment navigation policies, adaptive grasping, and deformable object manipulation, all trained in simulation without real-world robot data. The research shows measurable improvements: 3x speedup in multi-arm planning, 4.5x better navigation success rates, and 75% grasping success on real robots versus 41% baseline.","published_at":"2026-05-28T16:05:33.313+00:00","updated_at":"2026-05-28T16:05:33.012515+00:00","source":{"url":"https://blogs.nvidia.com/blog/icra-research-robotics-simulation-to-real-world/","name":"NVIDIA Blog (AI)"},"featured_image":{"url":"https://mrrqbmstywujcvowvptv.supabase.co/storage/v1/object/public/thumbnails/imports/1cc2930ebe4b3f806d020f6fce17ad51.jpg","alt":null},"categories":[{"name":"Research","slug":"research"},{"name":"AI Hardware","slug":"ai-hardware"},{"name":"AI Agents","slug":"ai-agents"}]},{"slug":"from-simulation-to-production-how-to-build-robots-with-ai","title":"NVIDIA Open-Sources Robot AI Stack to Bridge Simulation-to-Production Gap","url":"https://vff.ai/article/2026/04/14/from-simulation-to-production-how-to-build-robots-with-ai","content_type":"model_release","summary":"NVIDIA has released new open models and frameworks designed to streamline the development of production robots by integrating simulation, robot learning, and embedded compute into unified cloud-to-robot workflows. The tools aim to reduce friction in moving AI systems from simulated environments to real-world robotic hardware. This represents a shift toward making robot development more accessible and faster by consolidating previously fragmented tooling and infrastructure layers.","published_at":"2026-04-14T17:45:24.359+00:00","updated_at":"2026-04-22T00:59:04.768177+00:00","source":{"url":"https://blogs.nvidia.com/blog/build-robots-with-ai/","name":"NVIDIA Blog (AI)"},"featured_image":{"url":"https://blogs.nvidia.com/wp-content/uploads/2026/03/gtc26-models-and-frameworks-1920x1080-1.gif","alt":null},"categories":[{"name":"Infrastructure","slug":"infrastructure"},{"name":"Model Releases","slug":"model-releases"},{"name":"Open Source","slug":"open-source"}]}]}