| name | native-video-action-pretraining-generalizable-robot-control |
| description | LingBot-VA 2.0: video-action foundation model built from the ground up for robot embodiment. Semantic visual-action tokenizer, causal pretraining from scratch, sparse MoE backbone, asynchronous inference for real-time closed-loop control. Few-shot generalization across manipulation tasks. Activation: video-action model, robot control, pretraining, vision-language-action, generalizable manipulation. |
| metadata | {"arxiv_id":"2607.08639","published":"2026-07-09","authors":"Qihang Zhang, Lin Li, Luyao Zhang, Shuai Yang, Yiming Luo","tags":["video-action-model","robot-control","pretraining","vision-language-action","generalizable-manipulation"]} |
Native Video-Action Pretraining for Generalizable Robot Control
Overview
LingBot-VA 2.0 is a video-action foundation model built from the ground up for embodiment, addressing the inadequacy of repurposing video generative models designed for digital content creation for physical robot environments. It introduces four core design principles that differentiate it from both prior video models and VLA approaches.
Key Innovations
Semantic Visual-Action Tokenizer
- Departs from reconstruction-focused VAEs
- Aligns visual representations with both semantics and actions
- Improves instruction following and action precision in policy learning
Causal Pretraining from Scratch
- Adopts causal pretraining paradigm matching temporal dynamics
- Trained from scratch to circumvent catastrophic forgetting
- Avoids the adaptation issues of bidirectional architectures
Sparse MoE Backbone
- Expands model capacity without compromising inference efficiency
- Enables high-frequency inference for real-time control
