Robbyant, an embodied AI company within Ant Group, today announced the upgrade and open-source release of LingBot-VLA 2.0. Building upon the foundation of LingBot-VLA 1.0 released in January 2026, this next-generation vision-language-action (VLA) model delivers significant leaps in morphological generalization, degrees of freedom (DoF) support, and deployment efficiency, delivering a more advanced “universal brain” for scalable real-world robotics.
While the embodied AI industry is witnessing rapid advancements in hardware and control systems, the lack of a truly universal brain remains a primary bottleneck for industrial-scale deployment. LingBot-VLA 2.0 addresses this critical gap by dramatically expanding its pre-training data and architectural capabilities.
LingBot-VLA 2.0 was pre-trained on 60,000 hours of high-quality, real-world physical data. This massive dataset was curated from 50,000 hours of cleaned real-robot interaction data and 10,000 hours of distilled first-person human manipulation data.
Sourced from 20 distinct robot morphologies across 17 leading manufacturers—including Leju, AgiBot, Unitree, AgileX, Galaxea, Galbot, Astribot, RealMan, Franka, ARX, X-Humanoid, Fourier, MagicLab, Spirit AI, Zerith, Flexiv, and Qinglong—the data covers single-arm, dual-arm, bipedal, and wheeled configurations.
In terms of DoF support, LingBot-VLA 2.0 expands its operational capabilities to include head, waist, end-effectors (hands), and mobile chassis, enabling highly coordinated whole-body control.
In terms of dual-arm manipulation, on the Shanghai Jiao Tong University’s GM-100 benchmark, LingBot-VLA 2.0 achieved leading average task progress scores and success rates on AgileX Cobot Magic and Galaxea R1 Pro platforms, outperforming both π0.5 and GR00T N1.7, which demonstrates LingBot-VLA 2.0’s superior cross-morphology generalization.
In long-horizon mobile manipulation tasks tested on the ARX Arm + AgileX Chassis and Astribot S1 platforms, LingBot-VLA 2.0 surpassed π0.5 in both task progress scores and success rates. Its robust performance in challenging cross-domain scenarios highlights its advanced capability in executing long-sequence tasks and generalizing mobile manipulation.
To address the high costs associated with post-training and deployment, LingBot-VLA 2.0 introduces a version optimized for highly efficient post-training, with latency being strictly maintained under 130 milliseconds on an RTX 4090. This significantly lowers the barrier for commercial deployment.
Robbyant is actively exploring the applications of LingBot-VLA 2.0 in real-world business scenarios. In collaboration with hardware partners like Leju and Ti5 Robot, and enterprise customers including GuoDa Drugstore and Longsheng Technology, the model is undergoing comprehensive commercial pilot testing in retail sorting, logistics, and industrial automation scenarios. Furthermore, Robbyant is partnering with companies including GenRobot.ai to build standardized data ecosystems.
LingBot-VLA 2.0 is fully open-sourced today. To learn more about Ling-VLA 2.0, please visit:
GitHub: https://github.com/Robbyant/lingbot-vla-v2
Hugging Face: https://huggingface.co/collections/robbyant/lingbot-vla-v2
Looking ahead, Robbyant will host a series of developer meetups and introduce specialized technical toolkits tailored for the developer community.
About Robbyant
Robbyant is an embodied intelligence company within Ant Group, dedicated to advancing embodied intelligence through cutting-edge software and hardware technologies. Robbyant independently develops foundational large models for embodied AI and actively explores next-generation intelligent devices, aiming to create robotic companions and caregivers that truly understand and enhance people’s everyday lives and deliver reliable intelligent services across key use cases, such as elderly care, medical assistance, and household tasks.
To learn more about Robbyant, please visit: www.robbyant.com
Contacts
Media Inquiries
Vick Li Wei
Ant Group
Vick.lw@antgroup.com
LingBot-VLA 2.0 supports a wide range of robot morphologies