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VTAM model achieves efficient visuo-tactile manipulation without pretraining

By PulseAugur Editorial · [1 sources] ·

Researchers have developed VTAM, an efficient visuo-tactile world-action model designed for contact-rich manipulation tasks. This model does not require tactile or action pretraining and utilizes shared frozen video VAE and transformer weights for RGB and tactile observations. VTAM outperforms existing baselines on real-robot and simulation tasks, demonstrating comparable performance to models with extensive pretraining while being significantly faster during inference. AI

IMPACT This model could accelerate advancements in robotic manipulation by reducing the need for extensive tactile pretraining and improving inference speed.

RANK_REASON The cluster contains a research paper detailing a new model for robotics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VTAM model achieves efficient visuo-tactile manipulation without pretraining

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The cluster contains a research paper detailing a new model for robotics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haoran Yuan, Weigang Yi, Zhenyu Zhang, Wendi Chen, Yuchen Mo, Jiashi Yin, Xinzhuo Li, Yue Luo, Jialin Li, Xiangyu Zeng, Chuan Wen, Cewu Lu, Katherine Driggs-Campbell, Ismini Lourentzou ·

    VTAM: Efficient Visuo-Tactile World-Action Models for Contact-Rich Manipulation without Tactile Pretraining

    arXiv:2603.23481v2 Announce Type: replace-cross Abstract: Tactile vision-language-action models (VLAs) and tactile world-action models (WAMs) are a promising new paradigm for contact-rich manipulation, but existing models either underutilize touch, rely on large-scale tactile pre…

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