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LingBot-VA 2.0: New Foundation Model for Generalizable Robot Control

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced LingBot-VA 2.0, a new video-action foundation model specifically designed for robot control in physical environments. Unlike models adapted from digital content generation, LingBot-VA 2.0 incorporates a semantic visual-action tokenizer for improved instruction following and action precision. It also utilizes a causal pretraining paradigm to prevent catastrophic forgetting and a sparse Mixture-of-Experts (MoE) backbone for efficient high-frequency inference. The model's real-time closed-loop control capabilities have been validated through real-world deployment, demonstrating robust few-shot generalization on complex manipulation tasks. AI

IMPACT This model's specialized design for physical environments and demonstrated generalization could accelerate the development of more capable and adaptable robots.

RANK_REASON The cluster contains a research paper detailing a new model and its capabilities.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LingBot-VA 2.0: New Foundation Model for Generalizable Robot Control

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Qihang Zhang, Lin Li, Luyao Zhang, Shuai Yang, Yiming Luo, Shuaiting Li, Ruilin Wang, Junke Wang, Jiahao Shao, Gangwei Xu, Jiaming Zhou, Yishu Shen, Yudong Jin, Fangyi Xu, Shuailei Ma, Jiaqi Liao, Guanxing Lu, Zifan Shi, Yongkun Wen, Yujie Zhao, Weixuan … ·

    Native Video-Action Pretraining for Generalizable Robot Control

    arXiv:2607.08639v1 Announce Type: cross Abstract: The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation is inherently inadequate for physical environments.…

  2. arXiv cs.CV TIER_1 English(EN) · Yinghao Xu ·

    Native Video-Action Pretraining for Generalizable Robot Control

    The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation is inherently inadequate for physical environments. To bridge this gap, we present LingBot-VA 2.0, a …

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