/ Paper arXiv 2611.12806
Curator's notes
LingBot-Touch learns a unified tactile representation that works across different sensor types and improves dexterous manipulation.
A model that learns touch information from any sensor and helps robots handle objects better.LingBot-Touch learns a unified tactile representation that works across different sensor types and improves dexterous manipulation.LingBot-Touch is a foundation model that learns sensor‑agnostic tactile tokenstactile tokensDiscrete embeddings that encode tactile sensor readings for use in downstream models. for piezoresistive, magnetic, and visuotactile data, improving dexterous manipulation across policies.Curator's notesWritten by AI from the paperReport a mistake
What's new
It creates sensor-agnostic tactile tokenstactile tokensDiscrete embeddings that encode tactile sensor readings for use in downstream models. that can be plugged into various policy models, unlike prior work tied to specific sensors.
Key result
The unified tactile representation consistently enhances manipulation dexterity across simulation and real-robot tasks while remaining computationally efficient.
Why it matters
Developers can reuse one tactile model for many sensors, simplifying system integration and boosting robot skill performance.
Abstract
From the paperThe authors' own words.
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 policydiffusion policyA generative policy that samples actions by denoising a latent variable sequence. to vision-language-action (VLA) modelsvision-language-action (VLA) modelsModels that combine visual, textual, and action inputs to generate robot behaviors. 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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What you could build
Written by AIIntermediateRequires multi‑sensor data handling and policy integration but no extreme compute.
- Integrate LingBot-Touch tokens into a diffusion policy for object reorientation.
- Add tactile tokens to a vision‑language‑action model for pick‑and‑place with unknown objects.
- Create a sensor‑agnostic tactile dataset collector for benchmarking new tactile hardware.
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What three types of tactile sensors does LingBot-Touch unify in its representation?
It unifies piezoresistive, magnetic, and visuotactile sensors.
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