Sensors drift, elastomers remember, clocks disagree, and sim contact never met a real fingertip. Tensiq standardizes tactile observations, characterizes real sensors, and produces evaluations you can defend.
Different teams hit different failures. Same instrument underneath. Each entry point stands on its own, so keep your pipeline and take only what earns its place.
You have a gripper, a sensor eval kit, and nobody who wants to own the parser. You want touch working in week one, not month four.
Standardize. One predictable contract instead of per-vendor parsers. → talk to us you need: consistency at scaleYou are about to spend serious money collecting contact-rich data. Whether that corpus is usable in two years is decided at ingest, not later.
Characterize. Per-unit gain, hysteresis, drift, and health across your fleet. → talk to us you need: an external instrumentYour pipeline works. Your problem is everything around it: unexplained gaps, noisy rigs, results that don't replicate, claims you can't audit.
Evaluate. We run these measurements on the data you already log, without changing your control stack. → talk to usHysteresis and viscoelasticity. Elastomers relax under held load, respond differently on the second press, and change with loading rate. Your model is learning a mapping whose input is silently history-dependent, spending capacity on the sensor's constitutive quirks instead of on manipulation. None of it transfers when a pad is replaced. We expose and correct the repeatable parts of this behavior before the data reaches your model.
The tactile channel contributes nothing. You paid for the sensors, the integration, and the data collection, and the model learned to ignore all of it. Either touch was genuinely redundant for the task, or the channel arrived unsynced, hysteretic, and unit-inconsistent, so the model was right to discard it. You cannot tell those apart without a clean channel to compare against. We give you the clean channel and the comparison.
The sim-to-real gap is a scalar. The gap arrives at the end of the pipeline, after weeks of training, with no decomposition. Contact model? Sensor response? Randomization ranges? One worn pad? You cannot debug a gap you cannot decompose. We measure sim and real tactile streams in the same format and report where they correspond and where they diverge, by contact regime.
Timing and sync. Vision at 30 Hz, tactile at 200 Hz to 1 kHz, proprioception at 500 Hz, separate clocks, unmeasured driver latency. Slip onset happens in tens of milliseconds, so a 40 ms offset means the model sees the consequence before the cause. Policies still train on this and quietly plateau. Nearest-timestamp matching in the dataloader cannot fix what the recording didn't capture.
Unknown eval noise floor. Is the policy brittle or is rig 9's fingertip worn? Without a characterized apparatus you cannot tell a 6-point improvement from sensor drift, and teams burn weeks chasing regressions that were a dying pad. We establish the noise floor of your rigs and gate eval runs on sensor health, so the numbers you compare are comparable.
Force claims without instruments. Safety bounds and compliance claims rest on readings from sensors nobody characterized, with unknown drift. When a partner, reviewer, or regulator asks for the basis of the claim, there isn't one. TT-instrumented episodes make force-domain assertions checkable, with calibrated uncertainty, and the eval report is versioned and auditable.
One consistent interface instead of rebuilding sensor-specific pipelines every time the hardware changes.
Know which samples are valid, comparable, calibrated, and traceable, while the corpus is still being written.
Separate policy error from sensor drift, timing offsets, rig variation, and sim mismatch instead of guessing.
A single success rate cannot tell you whether the policy failed, the sensor drifted, or the rig changed.
Know whether the model catches contact, load onset, slip, and release at the right moment.
Know whether your model's confidence can be trusted before you build a constraint on it.
See where your simulator matches reality and where it diverges, by contact regime.
Stop rig variance from masquerading as policy variance.
Every run ships as a versioned report another site can reproduce.
Say what you're building. If one of the sentences up top was yours, name it. We'll map the fastest path to something useful: an integration, a characterization, or an eval pilot. A few technical details or a logged data session are enough to start.
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