Robbyant has released LingBot-VLA 2.0, a universal AI model for robots trained on 60,000 hours of real-world physical data from 20 different robot types made by 17 manufacturers.
The model delivers three times faster inference while maintaining latency under 150 milliseconds, and outperforms competing models on dual-arm and mobile manipulation tasks.
The company is piloting the technology with retail, logistics, and industrial partners to demonstrate real-world commercial viability.
What happened
Robbyant, an embodied AI company within Ant Group, has released LingBot-VLA 2.0, an upgraded vision-language-action (VLA) model trained on 60,000 hours of real-world physical data from 20 robot morphologies across 17 manufacturers. The model expands support for head, waist, end-effectors, and mobile chassis control, and improves inference efficiency by 3 times compared to the previous generation while keeping latency under 150 milliseconds.
Why it matters
The embodied AI industry has lacked a truly universal brain for industrial-scale robot deployment. LingBot-VLA 2.0 addresses this bottleneck by demonstrating superior cross-morphology generalization—on the Shanghai Jiao Tong University's GM-100 benchmark, it outperformed both π0.5 and GR00T N1.7 on dual-arm manipulation, and surpassed π0.5 on long-horizon mobile manipulation tasks. The 3× inference improvement and sub-150-millisecond latency significantly lower the barrier for real-time commercial applications.
What to watch
Robbyant is conducting comprehensive commercial pilot testing with hardware partners Leju and Ti5Robot, and enterprise customers GuoDa Drugstore and Longsheng Technology in retail sorting, logistics, and industrial environments. The company is also partnering with GenRobot.ai to build standardized data ecosystems.
Ask the AI about this article →
LingBot-VLA 2.0 represents a significant step toward solving a long-standing constraint in embodied robotics. The embodied AI industry has advanced hardware and control systems, but the lack of a universal AI brain capable of controlling robots across different morphologies has been a primary bottleneck for scaling deployment to industrial settings. Robbyant's approach of training on 60,000 hours of real-world physical data—drawn from 20 different robot types manufactured by 17 companies—is designed to create genuine cross-morphology generalization rather than task-specific models.
The performance improvements demonstrated on established benchmarks suggest the model delivers real capability gains. On the Shanghai Jiao Tong University's GM-100 benchmark, LingBot-VLA 2.0 outperformed both π0.5 and GR00T N1.7 on dual-arm manipulation, and surpassed π0.5 on long-horizon mobile manipulation tasks. The 3× improvement in inference efficiency while maintaining sub-150-millisecond latency directly addresses a commercial deployment barrier—real-time responsiveness at scale. This combination of generalization and efficiency may enable broader adoption of AI-controlled robots in retail, logistics, and factory settings where response time is critical.
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