Light REACT is our first step toward scalable deployment at the control level. Transformer-based Whole-Body In-Context Learning uses recent physical interactions to infer the effects of external forces, hardware impairments, and environmental constraints, then respond with adaptive whole-body skills. Specialist training, multi-teacher distillation, and preference alignment encourage upright recovery and locomotion whenever feasible while retaining crawling as a fallback, reducing human intervention and task interruptions.
LightNav-0, our first general-purpose navigation brain, demonstrates zero-shot generalization across diverse robot embodiments, tasks, and scenes. Behind this capability is scalable alignment, enabled by a Real2Sim2Real data engine that turns 2,000+ real-world scenes into 4,000+ hours of simulation-based navigation experience.
Round led by CAS Investment, with participation from China Merchants Venture Capital and Xiang He Capital; Light Origins to release results from scalable pre-training and demonstrations of cross-embodiment capabilities.
Why are foundation-model companies all moving into embodied AI right now, and how do we break the data scarcity? Roger Jiang: the data goldmine for general-purpose robots lies in 10 billion hours of internet video.
A world model must predict both the next state and the next action — and predicting action matters more. Embodied AI still has to go through scalable pre-training, alignment and deployment.
Ground a short human-motion seed in physics, grow it across terrain variation, and distill locomotion and whole-body skills into one deployable depth policy — running onboard Lightbot 0.