--- title: "lingbot-touch-small" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/228beefa-d50c-40bb-848b-6910620f9742" updated: "2026-11-12" --- # lingbot-touch-small > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns tactile sensor readings from magnetic, piezoresistive and visuotactile sensors into learned features for robot manipulation. For robotics researchers who already have a compatible inference setup. lingbot-touch-small 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-Small is a tactile representation model for robot hands. It takes touch readings from magnetic, piezoresistive and visuotactile sensors and turns them into learned features. It uses a separate tokenizer for each sensor type and one shared Transformer, and it learns by predicting in latent space. ### Who it suits It suits robotics researchers and engineers who work with fingertip touch sensors and want features for perception and manipulation. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Robotics | Robot hand developers | Turn touch readings into learned features | Feeding fingertip sensor data to a grasp planner | | Tactile research | Researchers | Compare three sensor types with one model | Testing one encoder on magnetic and piezoresistive data | | Sensor hardware | Sensor makers | Check how a new sensor fits the shared input layout | Adapting a sensor's geometry to the expected shape | | Manipulation learning | Policy developers | Read the features in a downstream task | Querying the features with attention for a task head | You can skip it if you want a ready-to-run package, since this repository holds weights and a configuration but no inference code. ### Where to use it - The separate LingBot-Touch inference implementation, which the card says is distributed on its own - Windows, macOS or Linux with Python, to download the files - Setups with five-finger hands, since the input layout lists thumb, index, middle, ring and pinky | Use case | Who | What they get back | | --- | --- | --- | | Tactile perception | Robotics teams | Latent features from touch signals | | Robot manipulation | Policy developers | Features that a task head can read | | Sensor comparison | Researchers | One encoder across three sensor types | ### How to set it up You need Python for the download tool and the separate inference implementation. This repository gives you the weights and their matching configuration, nothing more. - Python on Windows, macOS or Linux - The huggingface\_hub package - The compatible LingBot-Touch inference code, obtained separately - Sensor data adapted to the expected input shape 1. Install huggingface\_hub. 2. Download the model files. 3. Keep encoder.pt and config.json together and load them with the inference code. ```sh pip install -U huggingface_hub hf download robbyant/lingbot-touch-small ``` ### How to use it The visible card shows no run command, because the repository ships no inference code. The encoder takes normalized touch signals, taxel coordinates and a choice of sensing type. It returns latent features with their time, finger and spatial addresses. The card recommends attention-based querying to read out a task. | File | What it holds | | --- | --- | | encoder.pt | The parameter tensors of the frozen target encoder | | config.json | The inference architecture and the modality routes | ### Example You feed normalized fingertip readings for all five fingers, choose the magnetic sensing type, and run the encoder. It gives back token-level latent features that a downstream grasping model can query. ### For newcomers and experts If you are new to this, picture a robot fingertip that can feel pressure. This model turns raw touch numbers into a compact set of features that a robot program can use. A first small try is only the download step, since running it needs the separate inference code. For people who know the field, the model is JEPA-based, so it predicts in latent space instead of reconstructing raw signals. Magnetic data keeps three axes. Piezoresistive readings and decoded visuotactile values sit in the Z channel while X and Y stay at zero. Raw camera pictures are not accepted as input. Touch readings are normalized to the input shape → A tokenizer for the sensor type makes tokens → A shared Transformer encodes them → You get latent features with finger and spatial addresses ### Good to know Before inference, you must convert the sensor layout, hand side, point order and value ranges to what the encoder expects. The repository has no sensor adapters, sample datasets or training files. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the touch signals you feed it, plus the downloaded files - Not for: use without the separate inference code, or with raw RGB images as input ## README ````markdown # lingbot-touch-small Turns tactile sensor readings from magnetic, piezoresistive and visuotactile sensors into learned features for robot manipulation. For robotics researchers who already have a compatible inference setup. 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-Small is a tactile representation model for robot hands. It takes touch readings from magnetic, piezoresistive and visuotactile sensors and turns them into learned features. It uses a separate tokenizer for each sensor type and one shared Transformer, and it learns by predicting in latent space. Who it suits: It suits robotics researchers and engineers who work with fingertip touch sensors and want features for perception and manipulation. ```table Industry | Who uses it | How they use it | Example Robotics | Robot hand developers | Turn touch readings into learned features | Feeding fingertip sensor data to a grasp planner Tactile research | Researchers | Compare three sensor types with one model | Testing one encoder on magnetic and piezoresistive data Sensor hardware | Sensor makers | Check how a new sensor fits the shared input layout | Adapting a sensor's geometry to the expected shape Manipulation learning | Policy developers | Read the features in a downstream task | Querying the features with attention for a task head ``` You can skip it if you want a ready-to-run package, since this repository holds weights and a configuration but no inference code. Where to use it: - The separate LingBot-Touch inference implementation, which the card says is distributed on its own - Windows, macOS or Linux with Python, to download the files - Setups with five-finger hands, since the input layout lists thumb, index, middle, ring and pinky ```table Use case | Who | What they get back Tactile perception | Robotics teams | Latent features from touch signals Robot manipulation | Policy developers | Features that a task head can read Sensor comparison | Researchers | One encoder across three sensor types ``` How to set it up: You need Python for the download tool and the separate inference implementation. This repository gives you the weights and their matching configuration, nothing more. - Python on Windows, macOS or Linux - The huggingface_hub package - The compatible LingBot-Touch inference code, obtained separately - Sensor data adapted to the expected input shape 1. Install huggingface_hub. 2. Download the model files. 3. Keep encoder.pt and config.json together and load them with the inference code. ```sh pip install -U huggingface_hub hf download robbyant/lingbot-touch-small ``` How to use it: The visible card shows no run command, because the repository ships no inference code. The encoder takes normalized touch signals, taxel coordinates and a choice of sensing type. It returns latent features with their time, finger and spatial addresses. The card recommends attention-based querying to read out a task. ```table File | What it holds encoder.pt | The parameter tensors of the frozen target encoder config.json | The inference architecture and the modality routes ``` Example: You feed normalized fingertip readings for all five fingers, choose the magnetic sensing type, and run the encoder. It gives back token-level latent features that a downstream grasping model can query. For newcomers and experts: If you are new to this, picture a robot fingertip that can feel pressure. This model turns raw touch numbers into a compact set of features that a robot program can use. A first small try is only the download step, since running it needs the separate inference code. For people who know the field, the model is JEPA-based, so it predicts in latent space instead of reconstructing raw signals. Magnetic data keeps three axes. Piezoresistive readings and decoded visuotactile values sit in the Z channel while X and Y stay at zero. Raw camera pictures are not accepted as input. ```flow Touch readings are normalized to the input shape A tokenizer for the sensor type makes tokens A shared Transformer encodes them You get latent features with finger and spatial addresses ``` Good to know: Before inference, you must convert the sensor layout, hand side, point order and value ranges to what the encoder expects. The repository has no sensor adapters, sample datasets or training files. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the touch signals you feed it, plus the downloaded files - Not for: use without the separate inference code, or with raw RGB images as input ## On Hugging Face, 13 November 2026 - Task: robotics - Downloads in the last 30 days: 309 - Likes: 10 - License: apache-2.0 - Tags: tactile, robotics, representation-learning ## Links - Model: https://huggingface.co/robbyant/lingbot-touch-small - Files: https://huggingface.co/robbyant/lingbot-touch-small/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-small ``` 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-small on Hugging Face](https://huggingface.co/robbyant/lingbot-touch-small) ## 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-small (this page) | 309 | 10 | — | apache-2.0 | 2026-11-09 | | [LingBot-Touch-Tiny](https://mindket.com/p/54b041f8-36a1-4caf-b323-3d7eb06c01e0) | 380 | 13 | — | 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-small on Hugging Face](https://huggingface.co/robbyant/lingbot-touch-small) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/228beefa-d50c-40bb-848b-6910620f9742) - [Its page on Mindket](https://mindket.com/p/228beefa-d50c-40bb-848b-6910620f9742)