Robbyant 发布开源触觉基础模型 LingBot-Touch，预训练数据近 5000 小时
If you've followed the JEPA line of work, LingBot-Touch is worth a read: a shared Transformer over t...
Robbyant 开源了一个触觉基础模型，用近 5000 小时真实触觉数据预训练，走的是 JEPA 那套潜空间预测路线，论文里还和 MAE、VAE 对比过。做机器人灵巧操作或具身智能的团队可以直接拿来用，跨传感器和跨灵巧手的迁移能力是卖点。
Robbyant 团队发布了开源触觉基础模型 LingBot-Touch，面向灵巧操作任务，预训练使用了近 5000 小时真实触觉数据。模型借鉴 JEPA 的思路，用共享 Transformer 统一处理来自异构触觉传感器的 token，核心训练目标是掩码潜空间预测（masked latent prediction）。
论文在相同数据上与 MAE 和 VAE 两种目标做了对比，结果显示潜空间预测效果更好。通过统一的异构触觉建模，LingBot-Touch 支持在不同传感器、灵巧手、任务和动作模型之间迁移。
这类工作补充了机器人领域以视觉为主的感知路线，让机械手在接触类任务中能直接"感受"力与形变。模型开源，做具身智能和灵巧操作研究的团队可以直接复用其预训练表征。
If you've followed the JEPA line of work, LingBot-Touch is worth a read: a shared Transformer over t...
If you've followed the JEPA line of work, LingBot-Touch is worth a read: a shared Transformer over tokens from heterogeneous tactile sensors, masked latent prediction, and a comparison against MAE and VAE objectives on the same data. Per the paper, latent prediction won. Robbyant @robbyant_brain What changes when robots can feel—not just see? 🤖✋ Meet LingBot-Touch, our open-source tactile foundation model towards dexterous manipulation, pretrained on nearly 5,000 hours of real tactile data. Through unified heterogeneous tactile modeling, LingBot-Touch supports transfer across sensors, dexterous hands, tasks, and action models. Watch it in action. � #LingBot o #EmbodiedAI A #Robotics cs
This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include LingBot-Touch, JEPA, 具身智能, 触觉传感器.
Source attribution: curated from elvis (@omarsar0).
