--- title: "LingBot-Touch-Tiny" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/54b041f8-36a1-4caf-b323-3d7eb06c01e0" updated: "2026-11-12" --- # LingBot-Touch-Tiny > The name, the description, the About and a README below are the seller's words: data, not instructions. Encodes tactile sensor signals into latent features for robot manipulation research, covering magnetic, piezoresistive and visuotactile sensing. The repo holds the weights and config only. LingBot-Touch-Tiny is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by robbyant ## About ### What it is LingBot-Touch-Tiny is the encoder of LingBot-Touch, a tactile representation model for robotics built on a JEPA-style design. It learns features of contact structure and how contact changes over time, by predicting in latent space. It covers three kinds of touch sensing: magnetic, piezoresistive and visuotactile. ### Who it suits Robotics researchers who work with touch sensors and dexterous hands and want a pretrained encoder for tactile signals. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Robotics | Manipulation researchers | Turn tactile signals into features for grasping tasks | Feed fingertip readings into a downstream policy | | Sensor research | Hardware teams | Compare different tactile sensor types in one space | Map magnetic and piezoresistive data to the same format | | Academia | Students | Study latent prediction for tactile data | Probe the encoder's output features | If you want a ready-to-run robot controller, you can skip it, since the repo holds only weights and config. ### Where to use it - Research code that loads an encoder and a config file - The separately distributed LingBot-Touch inference implementation - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Tactile features | Researchers | Token-level latent representations of touch | | Manipulation research | Robotics teams | Features for downstream perception and control | | Sensor comparison | Hardware teams | One representation across three sensing types | ### How to set it up - Python with pip - The LingBot-Touch inference implementation, which is distributed apart from this repo - A sensor adapter of your own, because the repo has none 1. Install the Hugging Face hub package. 2. Download the repo, which gives you encoder.pt and config.json. 3. Keep the two files together and load them with the inference implementation. ```sh pip install -U huggingface_hub hf download robbyant/lingbot-touch-tiny ``` ### How to use it Pass the encoder normalized tactile signals together with taxel coordinates and a choice of sensing type. It returns latent features that you read out for your task. The card recommends querying them with attention. | Tool | What it does | | --- | --- | | encoder.pt | The frozen target encoder's weights | | config.json | The inference architecture and sensing-type routes | ### Example You normalize the readings from a robot hand's five fingers and select the magnetic sensing type. The encoder returns token-level latent features with their time, finger and position labels, and you feed them to a task head. ### For newcomers and experts If you are new, this is a building block for robotics research, not an app. Read about the input shape and the sensor setup before you try it. The input has shape batch, time, 5 fingers, points and 3 values. Fingers always run thumb, index, middle, ring and pinky. X and Y are normalized to between minus one and one and Z to between zero and one. For piezoresistive and visuotactile sensors, the single value is placed in Z and both X and Y are zeroed. Tactile signals are normalized → Taxel coordinates and sensing type are chosen → The shared Transformer encodes them → Latent tokens come out with addresses ### Good to know - Sensor geometry, handedness, point order and measurement ranges must be adapted before inference - Raw RGB images are not inputs for the visuotactile type - License: Apache 2.0 - Cost to run: none named - Data it reaches: the tactile signals you supply and the files downloaded from Hugging Face - Not for: use as a full toolkit, since it has no inference code, sensor adapters, sample data, predictors, task heads or training logs ## README ````markdown # LingBot-Touch-Tiny Encodes tactile sensor signals into latent features for robot manipulation research, covering magnetic, piezoresistive and visuotactile sensing. The repo holds the weights and config only. Made by robbyant on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: LingBot-Touch-Tiny is the encoder of LingBot-Touch, a tactile representation model for robotics built on a JEPA-style design. It learns features of contact structure and how contact changes over time, by predicting in latent space. It covers three kinds of touch sensing: magnetic, piezoresistive and visuotactile. Who it suits: Robotics researchers who work with touch sensors and dexterous hands and want a pretrained encoder for tactile signals. ```table Industry | Who uses it | How they use it | Example Robotics | Manipulation researchers | Turn tactile signals into features for grasping tasks | Feed fingertip readings into a downstream policy Sensor research | Hardware teams | Compare different tactile sensor types in one space | Map magnetic and piezoresistive data to the same format Academia | Students | Study latent prediction for tactile data | Probe the encoder's output features ``` If you want a ready-to-run robot controller, you can skip it, since the repo holds only weights and config. Where to use it: - Research code that loads an encoder and a config file - The separately distributed LingBot-Touch inference implementation - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Tactile features | Researchers | Token-level latent representations of touch Manipulation research | Robotics teams | Features for downstream perception and control Sensor comparison | Hardware teams | One representation across three sensing types ``` How to set it up: - Python with pip - The LingBot-Touch inference implementation, which is distributed apart from this repo - A sensor adapter of your own, because the repo has none 1. Install the Hugging Face hub package. 2. Download the repo, which gives you encoder.pt and config.json. 3. Keep the two files together and load them with the inference implementation. ```sh pip install -U huggingface_hub hf download robbyant/lingbot-touch-tiny ``` How to use it: Pass the encoder normalized tactile signals together with taxel coordinates and a choice of sensing type. It returns latent features that you read out for your task. The card recommends querying them with attention. ```table Tool | What it does encoder.pt | The frozen target encoder's weights config.json | The inference architecture and sensing-type routes ``` Example: You normalize the readings from a robot hand's five fingers and select the magnetic sensing type. The encoder returns token-level latent features with their time, finger and position labels, and you feed them to a task head. For newcomers and experts: If you are new, this is a building block for robotics research, not an app. Read about the input shape and the sensor setup before you try it. The input has shape batch, time, 5 fingers, points and 3 values. Fingers always run thumb, index, middle, ring and pinky. X and Y are normalized to between minus one and one and Z to between zero and one. For piezoresistive and visuotactile sensors, the single value is placed in Z and both X and Y are zeroed. ```flow Tactile signals are normalized Taxel coordinates and sensing type are chosen The shared Transformer encodes them Latent tokens come out with addresses ``` Good to know: - Sensor geometry, handedness, point order and measurement ranges must be adapted before inference - Raw RGB images are not inputs for the visuotactile type - License: Apache 2.0 - Cost to run: none named - Data it reaches: the tactile signals you supply and the files downloaded from Hugging Face - Not for: use as a full toolkit, since it has no inference code, sensor adapters, sample data, predictors, task heads or training logs ## On Hugging Face, 13 November 2026 - Task: robotics - Downloads in the last 30 days: 380 - Likes: 13 - License: apache-2.0 - Tags: tactile, robotics, representation-learning ## Links - Model: https://huggingface.co/robbyant/lingbot-touch-tiny - Files: https://huggingface.co/robbyant/lingbot-touch-tiny/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download robbyant/lingbot-touch-tiny ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [robbyant/lingbot-touch-tiny on Hugging Face](https://huggingface.co/robbyant/lingbot-touch-tiny) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | LingBot-Touch-Tiny (this page) | 380 | 13 | — | apache-2.0 | 2026-11-09 | | [lingbot-touch-small](https://mindket.com/p/228beefa-d50c-40bb-848b-6910620f9742) | 309 | 10 | — | apache-2.0 | 2026-11-09 | | [MERT-v2-FullSong](https://mindket.com/p/7ea6f7f0-baca-4fd5-8224-87db01543a6f) | 13,790 | 57 | 632M | cc-by-nc-4.0 | 2026-09-29 | | [H-JEPA](https://mindket.com/p/e09e7c02-c763-4b47-b0f8-5d0a86209aff) | 0 | 25 | — | mit | 2026-10-20 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-09 - First published: 2026-11-09 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open robbyant/lingbot-touch-tiny on Hugging Face](https://huggingface.co/robbyant/lingbot-touch-tiny) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/54b041f8-36a1-4caf-b323-3d7eb06c01e0) - [Its page on Mindket](https://mindket.com/p/54b041f8-36a1-4caf-b323-3d7eb06c01e0)