tactile intelligence infrastructure

Give physical AI a sense of touch.

Tensiq turns fragmented tactile streams into deterministic, model-ready observations. Give VLAs, diffusion policies, world-action models, and multimodal robot models one consistent interface for touch across supported sensors, datasets, and embodiments.

Discuss your tactile data stack Explore a design partnership

Consistent timing, validity, quality, uncertainty, and provenance for training, simulation, evaluation, and deployment.

raw vendor stream · uncalibrated counts

→
TT
→

tensiq tensor · standardized, flagged

force_z 0.00 N uncertainty ±0.00 N valid true drift nominal provenance cal_2026-06-30

Illustrative stream showing the TT contract semantics, not a live sensor.

the problem

Touch is the last fragmented modality.

Vision and language have standard formats, calibration conventions, and tooling. Tactile has a different SDK per vendor and a pipeline someone on your team maintains by hand.

integration

Every sensor is a new plumbing project

Custom parsers, vendor SDKs, per unit calibration scripts, glue code. Then you swap hardware or add a second vendor and do it all again. The pipeline work never compounds.

drift

Sensors drift and don't tell you

Gels wear. Elastomers age. Calibration goes stale. The stream keeps flowing and looks plausible, so your model quietly trains on readings that stopped meaning anything weeks ago.

model scaling

Touch does not fit the training corpus

Vision, language, proprioception, and actions already flow through common training pipelines. Tactile arrives in different shapes, rates, units, coordinate systems, and failure modes, so it resists aggregation across robots, sensors, and datasets.

the format

One tactile interface for every model stack.

The Tensiq Tensor gives supported tactile hardware a common, deterministic contract. Downstream systems receive defined fields, synchronized timing, explicit validity, quality signals, and complete provenance instead of vendor-specific streams.

  • physical semanticsContact quantities use defined units, frames, and sensor profile semantics.
  • uncertaintyStandard fields for confidence and uncertainty, so downstream systems know what information is available.
  • validity + healthMissing, malformed, saturated, or otherwise unusable data is represented explicitly rather than hidden inside plausible looking values.
  • provenanceEvery observation retains its sensor, profile, processing, timing, and source context.
# one contract, any supported sensor tt.sample { force: [0.02, -0.01, 1.84] # N uncertainty: ±0.06 N valid: true health: { drift: nominal } provenance: { sensor: "…", cal: "2026-06-30" } }

Evaluating before you talk to anyone? Request the TT format spec in the form below. No call required.

how it works

From tactile hardware to model input.

Connect

Connect a supported sensor or bring your own. When a new adapter is required, we scope and build it with your team.

Standardize

Tensiq transforms sensor-specific streams into a deterministic tactile contract while preserving timing, validity, quality, and provenance.

Integrate

Feed the resulting observations into model training, simulation, evaluation, logging, or real-time systems without rebuilding the downstream pipeline for each supported sensor.

The sensor-specific complexity stays behind the Tensiq interface. Your models consume one predictable tactile contract.
ground truth infrastructure

The truth layer between sensors and models.

Tensiq is the tactile data layer between sensors and models. It converts hardware-specific streams into consistent observations for training, simulation, evaluation, and deployment, preserving the timing, validity, uncertainty, quality, and provenance model builders need. Measurement-backed sensor characterization keeps those observations tied to physical interaction rather than vendor-specific signal conventions.

model and data partnerships

Build your tactile data layer with us.

We work with model, data, and simulation teams that need touch to become a usable modality, not another sensor-specific research project.

  • Convert supported tactile recordings into deterministic TT artifacts
  • Map tactile observations into VLA, diffusion policy, world-action model, or multimodal training pipelines
  • Diagnose synchronization, validity, dropout, and provenance problems
  • Create consistent exports across sensors, robots, and datasets
  • Define a pilot around a measurable model, data, or integration outcome
Explore a design partnership
built for physical AI teams

Your models understand vision. What about contact?

→You're training a VLA, diffusion policy, or multimodal robot foundation model and need a consistent tactile input layer.
→You're pretraining or post-training a multimodal model and need tactile data that aggregates across embodiments, sensor types, and collection systems.
→You're building a world or action model and need contact observations aligned with vision, proprioception, and actions.
→You're building simulation or synthetic-data infrastructure and need real tactile observations to anchor contact behavior.
→You operate robot data-collection systems and need to know which tactile samples are valid, comparable, and reusable.

Especially relevant for teams working with:
multimodal pretrainingcross-embodiment datasetscontact-rich post-trainingVLA trainingdiffusion policiesworld-action modelssynthetic datarobot foundation modelsIsaac Sim and Isaac LabGenesisMuJoCoLeRobotRLDSPyTorch

Common tactile sources:
GelSight-style optical sensorstaxel arraysforce-torque sensorsmulti-sensor hands

get in touch

Make touch usable by your models.

Tell us what models you train, which sensors produce the data, and where the tactile pipeline breaks. We'll map the fastest path to a useful integration, design partnership, or pilot.

Typical first reply: a few honest questions about your setup.
What are you building?
Select any that apply
No polished writeup needed. A few technical details are enough to start. Prefer email? info@tensiq.com We'll use this information only to respond to your request. Please don't submit confidential datasets or proprietary files through this form.