How to choose a tactile sensor

Write a one-sentence requirement: “When this contact occurs, the robot should take this action within this time, while meeting this outcome.” For example, a gripper might need to detect an incipient slip and adjust its grip before an object is lost. Name the failure you want to reduce, rather than starting with a preferred sensing technology.

Decide what evidence would make adding touch worthwhile. Record the current task result, failure types and interventions without the proposed tactile input. Use the same conditions when you evaluate the candidate. A sensor can produce attractive contact images without improving the decision your controller actually makes.

Sketch the active sensing area on the actual finger, jaw or body panel. Include edge contacts, curvature, cable exits, mounting hardware and the thickness added by a protective layer. Ask whether the sensor changes the grasp geometry or blocks the camera view. A sensing area that fits on a CAD model may still miss the contact during a real grasp.

Specify your expected load range and what happens during overload. Keep force in newtons separate from pressure in pascals: converting between them requires an appropriate contact area. Also distinguish spatial sampling, the smallest detectable change and measurement error. These describe different properties and should occupy separate rows in a comparison.

Use a mechanism to narrow a shortlist, then inspect the implementation. Optical, magnetic and electrical sensors can expose very different raw signals. Check whether the interface returns images, field readings, a pressure array or calibrated forces, and whether any advertised slip or material label requires an additional model.

The original DIGIT study provides an optical fingertip example; ReSkin illustrates a magnetic interface that can be replaced separately from its electronics. These are source examples, not a recommendation that one family is universally better. Compare the software and calibration that turn each raw signal into your required observation.

Ask for a sample recording before committing to an integration. It should let you identify time, sensor revision, units, geometry and invalid samples. Confirm how to obtain raw data, preserve calibration and detect a disconnected or stale sensor. Check the documentation and license for the exact driver or SDK you plan to use.

Measure from physical contact to the resulting robot response. A frame-rate figure excludes some combination of capture, transfer, queuing, reconstruction and control. Record delay variation and the slow cases as well as the typical case. Select acceptable limits before testing, based on the task, rather than retrofitting a pass threshold to the result.

Request a documented calibration procedure and find out which changes invalidate it: a new pad, remounting, changed temperature or a different loading direction. Include an unloaded baseline and a known reference contact in the evaluation. Check performance after the maintenance operations your team will actually perform.

Compare the complete effort: hardware, spare contact surfaces, mounting, compute, cables, calibration equipment, driver work and servicing time. Obtain a current quote and replacement terms from the supplier. This guide does not assign prices or availability to research prototypes.

Use a small representative object and contact set, including the cases that currently fail. Separate tuning trials from evaluation trials. Record sensor version, mount, software, conditions, trial count and pass criteria so a colleague can repeat the comparison. If possible, change only the sensing input while holding the rest of the system constant.

Report task success with its denominator, plus damage, interventions, missed events, false alarms and response delay where relevant. Inspect edge contacts and repeated loading. A pass applies to the tested conditions; it is not evidence for arbitrary objects, environments or deployment duration.

Download the blank worksheet below and fill in requirements before contacting a vendor or building a prototype. Leave an unknown as unknown. A candidate with an unmeasured critical requirement should remain on hold until the measurement is available.

For surgical tools, start with the dedicated application notes and the exact experimental setting. A laboratory tissue experiment does not establish human clinical readiness. For wearable demonstration capture, inspect how mounting and contact representation transfer between a human and a robot.

When a system advertises super-resolution, ask what is reconstructed and how it was evaluated. A dense predicted image is not a dense array of independent physical measurements. Keep the raw sampling layout, held-out localization error and downstream task result separate.