Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
LingBot-Touch is a tactile foundational model that learns a unified, scalable representation across different tactile sensors (resistive, magnetic, visual tactile), producing plug-and-play tactile tokens, and continuously enhancing the dexterity of diffusion strategies and visual language action (VLA) models (such as π_{0.5} and LingBot-VLA). Experiments show that this tactile representation significantly improves performance in complex manipulation tasks in both simulation and real robot deployment while maintaining strong computational efficiency.
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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