An instrumented reference object that gives robots certified ground truth for training, calibration, and validation. The data labeler of physical AI.
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Robots learn to interact with the world by collecting real-world data. Unlike language or vision models, every physical interaction must be measured with real hardware. The problem: no one knows if those measurements are right.
Every lab builds its own measurement setup from scratch, with different sensors and unknown accuracy.
Without verified ground truth, the sim-to-real gap forces teams into expensive real-world trial cycles.
Teams can't confirm whether the contact data their models learn from reflects physical reality.
The field is advancing despite a foundational measurement gap, one it has yet to solve.
PRISM is a modular instrumented object that robots grasp, push, and manipulate. Internal sensors measure every channel of the contact event simultaneously, each one calibrated with documented uncertainty.
Teams use PRISM to calibrate their own sensors, parameterize simulation, and validate training data against a known physical standard. It is the instrumented successor to the passive object sets the field relies on today.
Every channel reports its value with a known, documented uncertainty bound, referenced to a certified standard. Not estimated. Measured.
A robot handles PRISM the way it would any object. PRISM captures what happens across every sensing channel in real time.
Force, torque, pressure, contact location, and temperature, captured simultaneously and reported as certified ground truth with known accuracy.
Teams use that ground truth to calibrate sensors, tune simulation, and verify the data their models learn from.
Each shell presents a different shape, surface, and grasp challenge while the calibrated core stays constant, so a single platform spans the full range of manipulation scenarios.
The diversity in the data comes from the robots that use PRISM. Like a color calibration target in photography, PRISM doesn't reduce variety, it makes that variety measurable and comparable across every system.
A reusable sensing core pairs with interchangeable certified shells, connected through rigid interfaces that preserve high-frequency signal fidelity. One platform spans the manipulation scenarios robotics teams train for.
Running manipulation research that needs citable, credible ground truth.
Training manipulation models that depend on trustworthy contact data.
Validating and calibrating the manipulation performance of their systems.
PRISM is built by Perceptive Hardware, a sensing and hardware development studio led by a PhD engineer with more than two decades developing precision instrumentation, MEMS sensors, and calibrated measurement systems across aerospace, defense, and commercial programs.
Measuring the physical world accurately is the problem we have spent our careers solving. PRISM brings that expertise to physical AI.
We are working with early partners in robotics and physical AI. If you are training, calibrating, or validating manipulation systems, we would like to talk.
Request early access