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  "text": "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 intelligence - in a single model that translates any motion command into stable, natural motor signals. And it's all open-source!! \n\nThe key insight: motion tracking is the one, true scalable task for whole body control. Instead of hand-engineering rewards for every new skill, we use dense, frame-by-frame supervision from human mocap data. The data itself encodes the reward function: \"configure your limbs in any human-like position while maintaining balance\".\n\nWe scaled humanoid motion RL to an unprecedented scale: 100M+ mocap frames and 500,000+ parallel robots across 128 GPUs. NVIDIA Isaac Lab allows us to accelerate physics at 10,000x faster tick, giving robots many years of virtual experience in only hours of wall clock time. After 3 days of training, the neural net transfers zero-shot to the real G1 robot with no finetuning. 100% success rate across 50 diverse real-world motion sequences. \n\nOne SONIC policy supports all of the following:\n\n- VR whole-body teleoperation\n- Human video. Just point a webcam to live stream motions.\n- Text prompts. \"Walk sideways\", \"dance like a monkey\", \"kick your left foot\", etc.\n- Music audio. The robot dances to the beat, adapting to tempo and rhythm.\n- VLA foundation models. We plugged in GR00T N1.5 and achieved 95% success on mobile tasks.\n\nWe open-source the code and model checkpoints!! Deep dive in thread:",
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