Tactile sensing lets a robot measure physical contact through signals such as pressure, deformation, vibration or changes in an optical image. The available measurements depend on the sensor and its calibration.
Contact changes a sensing surface or element. Electronics or a camera capture that change; processing converts it into a usable observation. A controller can then use the observation to adjust a grasp or investigate an unexpected contact.
A tactile sensor supplies observations. Robot skin distributes sensing over a surface. Tactile AI uses learned models to interpret touch or support decisions. None of these labels alone establishes calibrated force, reliable slip detection or better task performance.
For example, an optical fingertip can record how a gel deforms during a grasp. Estimating force from those images needs calibration or a model; evaluating slip recovery also needs a controller and task-level tests.
Source example: the original DIGIT paper. This introduction summarizes sensing concepts; RoboSkin.ai has not performed a hardware comparison for this page.
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Flexible tactile elements capture contact, pressure, and interaction events.
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Local processing turns raw sensor output into cleaner robot-ready data.
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Mechanical design supports curved surfaces and application-driven layouts.
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The site is organized around robotics workflows, research and category context.
Step 1
Flexible tactile elements sit on the robot hand, gripper, arm, or curved body surface.
Local pressure, shear, slip, temperature, or contact-event signals depending on configuration.
Step 2
Electronics and firmware clean raw readings, align timestamps, and preserve calibration metadata.
Robot-ready tactile frames, event streams, or reduced contact features.
Step 3
The integration layer maps tactile data into the team pipeline, including ROS 2, replay, logging, and controller interfaces when applicable.
Documented topics, coordinate frames, QoS expectations, and debug workflow.
Step 4
The robot stack uses tactile features for grip confidence, slip response, contact-aware motion, or evaluation analytics.
Task-specific criteria for research interpretation, category framing, or source review.
Physical AI contact layer
For Physical AI systems, robot skin is the contact layer. It helps expose touch, pressure, shear, slip, and timing signals that vision alone cannot measure during interaction.
Open the Physical AI tactile contact layer ->Fit criteria
Target surface area, curvature, attachment method, cable routing, and serviceability shape the relevant category or content route.
Teams should define whether they need pressure maps, shear, slip events, temperature, force/torque context, or lower-bandwidth contact events.
Useful research context defines message formats, timestamps, coordinate frames, logging, replay, and calibration handling before planning claims.
Durability, latency, sensitivity, drift, and environmental claims should be measured against the exact robot and use case.
Use the research and glossary routes for terminology, or send a research inquiry if RoboSkin.ai fits your project.