Runnable project · Python verified
Inspect the dataset format ->LeRobot episodes, timestamps and validation
Understand v3 storage and run a bounded numeric checker on valid and broken synthetic Parquet fixtures.
Resources
Resource-library visual for public learning routes and technical references.
Tactile robotics resources by task
Explore research resources ->Choose what you need to do, then follow a guide, sensor record, dataset, or paper review. The complete directory below is grouped by subject so you can move from a question to the relevant evidence.
What are you working on?
Learn tactile sensing
Three learning paths from terminology to hardware and usable robot data.
Investigate a contact problem
Start with slip, missing body contact, calibration drift, or data access.
Compare sensor hardware
Find sensor principles, documented outputs, software, and source evidence.
Find data for a robot policy
Check modalities, file availability, licenses, tasks, and evaluation splits.
Programming & Tutorials
Source-based workflow · no hardware experiment
Prepare a calibration workflow ->DIGIT and GelSight Mini calibration
Separate image, depth and force targets and download a blank calibration record with units, repeats and references.
Learning path
Explore the programming path ->Robotics programming: from code to feedback
Choose Python and ROS 2 tools, learn without hardware, and connect sensor data to robot learning.
Runnable project · Python verified
Run the Python exercise ->Process tactile data with Python
Download a complete synthetic CSV exercise with quality checks, generated plots and contact-event exports.
Source-checked walkthrough · runtime pending
Read the ROS 2 walkthrough ->ROS 2 tactile messages and replay
Build the starter kit, inspect its message contract and follow the recording workflow. Local ROS runtime checks remain pending.
Category guides
Public guide
Explore ->Robot skin overview
Read a plain-language route into robot skin, tactile AI, e-skin, and tactile sensing terminology.
Public guide
Explore ->RoboSkin.ai source context
Understand how the site supports robotics education, research notes, and source-backed category pages.
Research routes
Terminology references
Public reference
Read ->Glossary
Review definitions for robot skin, tactile AI, e-skin, slip detection, and multimodal tactile sensing.
Public explainer
Read ->Physical AI
Understand Physical AI as a full physical perception, reasoning, policy, control, embodiment, safety, and feedback system.
Public reference
Read ->FAQ
Read guidance about how to interpret public robot skin and tactile AI source boundaries.
Corrections and collaboration
Contact path
Send note ->Correction inquiry
Use the contact route for corrections, partnership, or content collaboration around RoboSkin.ai.
Contact path
Send note ->Research note submission
Suggest sources, corrections, or additions that improve robot skin and tactile AI category coverage.
Complete guide directory
Browse by subject
Understand robot skin and touch
Definitions and system concepts before choosing a component or model.
AI and robotics hub
How artificial intelligence works in robots
AI provides perception, reasoning, prediction, and learned policies; robotics provides sensors, control, actuators, safety, and a physical body. Map the closed loop from instruction to action and touch feedback.
Core conceptWhat is robot skin?
Robot skin is a tactile sensing surface for robots. Learn how robot skin relates to tactile AI, e-skin, humanoid hands, grippers, and contact-aware robotics.
Core conceptTactile AI: touch data for Physical AI
Tactile AI turns robot touch signals into perception, learned representations, and action. Explore models, datasets, benchmarks, robot platforms, and Physical AI research.
Core conceptE-skin in robotics
E-skin, or electronic skin, is a flexible sensor surface. Learn how e-skin connects to robot skin, soft robotic skin, tactile sensors, and humanoid robots.
Technology guidePhysical AI and touch
Touch grounds Physical AI in real contact. Learn how tactile sensing combines with vision, language, proprioception, world models, robot learning, and control.
Evaluation guideRobot touch sensors: which signal do you need?
Compare binary contact switches, force measurements, and tactile arrays. Choose the signal your robot needs and plan a first contact-sensing test.
Comparison guideRobot skin vs e-skin
Compare robot skin and e-skin. Learn where the terms overlap, where they differ, and how tactile AI connects electronic skin to robot behavior.
Comparison guideRobot skin vs tactile sensor
Compare robot skin and tactile sensor terms. Learn when a robot needs a tactile sensor, when it needs robot skin, and how tactile AI connects the system.
Tactile feedback · evidence reviewFrom Tactile Sensing to Robot Action: What the Evidence Shows
Does better touch sensing improve robot manipulation? Compare detection, control and task evidence from six studies, with trial counts, training limits and verified resource access.
Select and compare tactile sensors
Sensor hardware, measurement tradeoffs, and evidence for a shortlist.
Sensor evidence / Vision-based fingertip
DIGIT tactile sensor: from RGB frames to robot touch
Evaluate the original DIGIT tactile sensor: paper specifications, Python stream settings, design files, calibration needs, repository status, and reuse terms.
Sensor evidence / Commercial optical touchGelSight Mini: specifications and a usable research workflow
Review GelSight Mini specifications, RGB and 3D workflows, calibration limits, replaceable gel, Python examples, and evidence for robotics use.
Sensor evidence / Magnetic tactile skinReSkin: magnetic touch with a replaceable skin
Understand ReSkin magnetic tactile sensing: replaceable elastomer, five-magnetometer design, 400 Hz research setup, Python data collection, and calibration evidence.
Technology guideFlexible tactile sensor array guide
Flexible tactile sensor arrays measure contact across curved robot surfaces. Learn how arrays relate to robot skin, e-skin, calibration, and tactile AI.
Evaluation guideTactile sensor for robots
A tactile sensor for robots measures pressure, force, slip, strain, or contact maps. Compare sensors for robot hands, grippers, and robot skin.
Sensor comparison guideTactile sensor benchmark for robot manipulation
Compare visual, acoustic, magnetic, and resistive tactile sensors by manipulation task, signal, integration constraint, and evidence boundary.
Source-reviewed sensor directoryTactile sensors for robots compared
Compare tactile sensors for robot hands, grippers, and skins by sensing principle, signal, form factor, rate, integration, access, and evidence boundary.
Match sensing to the robot and task
Hands, grippers, soft bodies, manipulation, and contact evaluation.
Application guide
Humanoid robot skin and whole-body tactile sensing
Humanoid robot skin brings tactile sensing to hands, arms, and body surfaces. Map the whole-body tactile stack, safety boundaries, sensors, datasets, and research.
Application guideRobot hand tactile sensor guide
Robot hand tactile sensors help dexterous hands detect contact, slip, force patterns, and grasp stability. Learn where fingertip, palm, and full-hand sensing differ.
Application guideSoft robotic skin
Soft robotic skin uses flexible sensing surfaces for curved robots, grippers, prosthetics, and soft machines. Learn how it differs from generic e-skin.
Evaluation guideRobot gripper tactile sensor guide
Robot gripper tactile sensors help detect contact, pressure patterns, slip, and grasp stability. Learn what to evaluate before choosing tactile sensing for grippers.
Evaluation guideSlip detection for robot hands
Slip detection helps robot hands and grippers react before an object drops. Learn the tactile signals, validation questions, and robot-control constraints.
Tactile AI pillarTactile manipulation: from contact to robot action
Learn how tactile manipulation turns contact, pressure, shear, and slip into closed-loop robot actions for grasping, insertion, dexterity, and Physical AI.
High-interest robotics pillarHumanoid robots: intelligence, manipulation and touch
Understand humanoid robots through perception, robot learning, whole-body control, dexterous hands, safety, tactile sensing, and Physical AI evidence.
High-interest robotics task pillarRobot manipulation: learning, control and tactile feedback
Explore robot manipulation across grasping, dexterous hands, insertion, robot learning, VLA policies, force control, tactile feedback, and evaluation.
Robotics hardware pillarRobot hands: dexterity, sensing and evidence
Compare robot hands and grippers by actuation, sensing, control, task fit, and evidence. Learn how tactile robot hands support dexterous manipulation.
Robotics assurance pillarRobot safety: standards, sensing and validation
Understand industrial and humanoid robot safety, ISO 10218 scope, risk reduction, collision and contact sensing, validation, and robot-skin evidence boundaries.
Compare models, datasets, and benchmarks
Training inputs, model roles, artifact access, and evaluation protocols.
Source-reviewed model directory
Robot foundation models and the robot AI model stack
Compare OpenVLA, Octo, RT-2 and other robot AI models by training data, robot compatibility, code and weight access, tactile input, and evaluation evidence.
Source-linked dataset directoryTactile datasets for robot learning
Find tactile and visuo-tactile datasets for robot learning. Filter by sensor, robot and task; compare primary download links, license status and split design.
Tactile AI model guideTactile foundation models for robotics compared
Compare tactile foundation models and related robot-learning systems by representation, prediction, policy role, evidence, and transfer limits.
2026 world-model guideVisuo-tactile world models for robot manipulation
Compare VT-WM, Dream-Tac, TouchWorld, ViTacWorld and FeelWorld: what each predicts, how it guides robot actions, reported results and limits, with primary sources.
Structured benchmark directoryTactile robotics benchmarks compared
Compare tactile robotics benchmarks by task, sensor, robot, modality, metric, split protocol, access, and evidence boundary.
Multimodal tactile AI pillarVisuo-tactile robotics: combining sight and touch
Understand visuo-tactile robotics: how robots align vision and touch for contact perception, representation learning, world models, and manipulation.
Vision-language-action guideVLA robotics: models, code and tactile feedback
What is VLA in robotics? Learn how vision-language-action models work, compare 11 models, and find OpenVLA, UniTacVLA and VLA-Touch code and access details.
High-interest robotics pillarRobot learning: data, models and real-world evaluation
Learn how robots learn from demonstrations, reinforcement, datasets, simulation, and touch, with source-backed guidance for real-world evaluation.
Robot data pillarRobotics datasets: data for robot learning and evaluation
Compare robotics datasets by robot, task, modality, action space, timing, access, and license. Find robot learning, manipulation, teleoperation, VLA, and humanoid data.
Robot learning model pillarRobot world models: prediction for physical action
Learn how robot world models predict future states for planning and control, how they differ from VLA and foundation models, and where tactile prediction fits.
Robot data collection pillarRobot teleoperation: from human demonstration to robot data
Learn how robot teleoperation captures demonstrations for robot learning and VLA training, including interfaces, synchronization, quality control, limits, and evaluation.
Program, calibrate, and record data
Python, ROS 2, calibration, and dataset-format workflows.
Programming & tutorials
Robotics Programming: From First Code to Sensor Feedback
Start robotics programming with Python, ROS 2 and sensor feedback. Follow a hardware-free tactile data exercise, then explore messages, replay and robot learning.
ROS 2 tutorialROS 2 tactile sensing: from synthetic messages to replay
Build the RoboSkin ROS 2 starter kit, inspect normalized tactile arrays and use rosbag2 to record and replay. Includes verified source references and explicit runtime limits.
Python tutorialProcess Tactile Sensor Data with Python: CSV, Heatmaps and Contact Events
Run a hardware-free Python exercise with synthetic tactile CSV data. Validate missing values and timestamps, plot a heatmap, detect teaching contact events and export results.
Robot data tutorialLeRobot Dataset Format Explained: Episodes, Timestamps and Validation
Understand LeRobot dataset v3 metadata, Parquet, video and episode timing. Run a small Python checker on synthetic numeric data and inspect its limits.
Tactile engineering guideTactile Sensor Calibration: DIGIT and GelSight Mini Workflows
Plan DIGIT and GelSight Mini calibration: separate image baselines, depth reconstruction and force estimation, then record references, repeats and uncertainty.
Trace the research and project history
Paper discovery and the sources behind named research projects.
Research guide
Robot skin papers and tactile sensing research index
Browse source-backed robot skin papers and research routes for tactile sensing, e-skin, soft robotic skin, robot hands, and tactile AI.
Historical research projectThe EU FP7 ROBOSKIN project: history, scope and sources
A source-backed record of the EU FP7 ROBOSKIN project, its robot-skin research scope, funding, partners, 2013 Springer paper, and relationship to RoboSkin.ai.
Trusted external references
Reference points for robot skin learning
RoboSkin.ai keeps external links narrow and useful. These resources help readers align terminology and avoid unsupported product claims.
For robotics and embedded-system hardware development, Vigor Components provides access to electronic components, connectors, power devices, and sourcing resources that can support BOM planning and prototype-to-production workflows.
For motor-drive and industrial power electronics component information, see SHYSEMI power semiconductors (IPM, IGBT and SiC).
Choose a learning route
Start with source-backed research, terminology, or a research inquiry depending on what you need to understand.