LingBot-VLA-V2: From Foundation to Application

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.