Wanxun Technology (万勋科技) launches NOVA2.0 embodied-AI brain, claims 10 million real-world robot task executions
The AMW Read
Second-gen product launch with concrete deployment scale (10M+ task executions, 40+ industries) advances the single-brain/multi-form-factor thesis for the segment, but doesn't resolve an open debate or introduce a new top-tier entrant.
Wanxun Technology (万勋科技) launches NOVA2.0 embodied-AI brain, claims 10 million real-world robot task executions
Wanxun Technology released NOVA2.0, its second-generation "flexible embodied brain" architecture, describing it as the first open-world, all-weather, commercial-delivery-grade embodied-AI system in the sector. The company says the architecture has already powered more than 10 million real-world task executions across over 40 industry scenarios, including construction, power and energy, transportation, autonomous driving, and manufacturing. NOVA2.0 pairs with Wanxun's Pliabot soft-robot bodies and can drive multiple form factors — muscle-joint units, soft single- and dual-arm systems, and dexterous hands or humanoid platforms — from one shared brain. The company cites 20-millisecond trajectory generation, a claimed ability to generalize to new scenarios using roughly 1% of conventional training data, and operating tolerances from -40°C to 60°C and altitudes above 5,000 meters.
The launch reflects embodied AI's shift from lab demos and pilot proof-of-concepts toward volume commercial deployment in unstructured, high-variance environments. Wanxun's pitch — one brain generalizing across many physical form factors, improved by a data flywheel drawn from live commercial operations rather than simulation — is a direct bet that field data diversity, not model scale alone, is the binding constraint on robot generalization in the open world.
For builders and investors evaluating physical-AI plays, Wanxun's disclosed traction — over 50 large enterprise customers, deployment across more than 100 countries, and repeat orders exceeding 40% of commercial order value — is a self-reported commercial signal, not an audited benchmark. The variable worth tracking is whether cross-form-factor data reuse actually compounds into faster generalization than rivals training on curated or simulated datasets, since that is the core defensibility claim behind the "one brain, many bodies" architecture.