LingBot-VLA-V2: From Foundation to Application

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LingBot-VLA-V2 is a practical Vision-Language-Action foundation model designed to move from large-scale pre-training toward reliable real-world robot applications.

Compared with LingBot-VLA, V2 improves three core capabilities:

• Generalization across tasks and embodiments: a redesigned data pipeline curates around 60,000 hours of pre-training data, including 50,000 hours of robot trajectories across 20 robot configurations and 10,000 hours of egocentric human videos.
• Expanded action space: the unified representation supports arms, end-effectors, grippers, dexterous hands, waist, head, and mobile-base signals instead of only standard dual-arm manipulation.
• Predictive dynamics modeling: future prediction is used as a proxy task, with DINO-Video providing semantic temporal priors and LingBot-Depth providing geometric cues.

MoE Action Expert

To improve cross-embodiment scaling, LingBot-VLA-V2 uses sparse MoE layers inside the action expert. Fine-grained expert segmentation and shared expert isolation allow universal priors and specialized embodiment/task patterns to coexist under the same active compute budget.

Dual-Query Distillation

LingBot-VLA-V2 appends current and future perceptual queries to the visual/text tokens. These queries are distilled from LingBot-Depth and DINO-Video, encouraging causal inference to capture both current scene geometry and future scene evolution.

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Model Sources

• Paper: From Foundation to Application: Improving VLA Models in Practice
• Repository: https://github.com/robbyant/lingbot-vla-v2
• Project Page: https://technology.robbyant.com/lingbot-vla-v2

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License Agreement

This project is licensed under the Apache-2.0 License.

Acknowledgement

This codebase is builded on the VeOmni project. Thanks for their excellent work!