LingBot-Touch 发布：一种支持通用灵巧操作的异构触觉表示学习模型
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
LingBot-Touch 是一个触觉基础模型，学习跨不同触觉传感器（压阻式、磁性、视觉触觉）的统一、可扩展表示，产出可即插即用的触觉 token，持续增强扩散策略和视觉语言行动（VLA）模型（如 π_{0.5} 和 LingBot-VLA）的灵巧操作能力。实验表明，该触觉表示在模拟和真实机器人部署中显著提升复杂操作任务表现，同时保持强计算效率。
Published on Nov 9
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Abstract
LingBot-Touch is a tactile foundation model that learns a unified, extensible representation across heterogeneous tactile sensors, yielding plug-and-play tactile tokens that consistently enhance manipulation policies such as diffusion policy and VLA models.
Tactile sensing is an indispensable modality for contact-rich dexterous manipulation, a capability that hints at the ultimate frontiers of embodied intelligence. However, commercially available tactile sensors are still at a relatively early stage of development, with each type of sensor offering irreplaceable strengths alongside its own drawbacks. A key challenge is thus to free tactile understanding from sensor-specific physics, so that embodied models can effectively leverage heterogeneous tactile data. To this end, we develop LingBot-Touch, a tactile foundation model characterized by the following properties. (i) Our model yields a unified representation across different tactile sensors (i.e., piezoresistive, magnetic, and visuotactile sensors), and is easily extensible to new sensor types. (ii) The resulting tactile tokens are plug-and-play: they can be seamlessly integrated into policy models across paradigms, from diffusion policy to vision-language-action (VLA) models such as π_{0.5} and LingBot-VLA, consistently enhancing manipulation dexterity. (iii) Extensive experiments, spanning simulation and real-robot deployments across multiple hardware platforms, demonstrate that our tactile representation substantially benefits complex manipulation tasks while maintaining strong computational efficiency. We believe the findings in this work demonstrate the potential of leveraging cross-sensor data to scale tactile learning, and offer encouraging insights for the design of future hardware and embodied models.
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来源: Hugging Face Daily Papers · huggingface.co