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An interactive world model developed by NVIDIA in collaboration with academic partners. – DreamDojo turns egocentric human video data into physical intelligence. – Human data is more scalable than robotics data but lacks action labels. – To solve this, a dedicated action model
https://x.com/TheHumanoidHub/status/2025368793321799909
Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It’s Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is
https://x.com/DrJimFan/status/2024895359236051274
NVIDIA has open-sourced SONIC, a humanoid behavior foundation model that gives robots a core set of motor skills learned from large-scale human motion data. https://x.com/TheHumanoidHub/status/2024935738362765677
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control https://nvlabs.github.io/GEAR-SONIC/
We have seen rapid progress in humanoid control — specialist robots can reliably generate agile, acrobatic, but preset motions. Our singular focus this year: putting generalist humanoids to do real work. To progress toward this goal, we developed SONIC ( https://x.com/yukez/status/2024639427788857707
We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We
https://x.com/DrJimFan/status/2026709304984875202
What can half of GPT-1 do? We trained a 42M transformer called SONIC to control the body of a humanoid robot. It takes a remarkable amount of subconscious processing for us humans to squat, turn, crawl, sprint. SONIC captures this “”System 1″” – the fast, reactive whole-body
https://x.com/DrJimFan/status/2026350142652383587
NVIDIA just released a Blackwell-optimized Qwen3.5 MoE on Hugging Face 397B parameters quantized to NVFP4 for 2x faster inference with SGLang.
https://x.com/HuggingPapers/status/2025825405836648849
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