# Tensiq ```{raw} html

COMPANY · CURRENT WORK at Tensiq · Foundation Model Engineer

Universal tactile intelligence
for robotics.

Tensiq turns any tactile sensor into a single, physics-grounded, model-ready data format — the “MP4 for touch.” One pipeline. Any sensor. Standardized output. Designed for the next generation of dexterous robots and tactile foundation models.

SHIPPING PRODUCTION TACTILE AI
◆ VISIT TENSIQ ✉ GET IN TOUCH
``` ## What Tensiq Does Tactile sensors come in dozens of incompatible flavors — vision-based gels, piezoresistive arrays, multi-zone hands, custom research rigs. Every format has its own quirks, every algorithm needs its own integration, and almost none of the data ends up usable for modern foundation-model training. **Tensiq solves that.** A single, sensor-agnostic pipeline turns raw tactile capture into a standardized tensor format with full physics grounding, quality auditing, and provenance — usable by real-time controllers, IL/RL pipelines, and tactile foundation models alike. ```{raw} html
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Sensor-agnostic

One pipeline, many sensors. Vision-gel, piezoresistive, multi-zone — all converge to the same standardized output.

⟁

Physics-grounded

Constitutive models compute real physical quantities — force, torque, contact state, slip — not just raw signal.

∎

Model-ready

Output is pre-tokenized for transformers and diffusion policies. Plug into PyTorch in one line.

✓

Auditable

Every frame carries provenance — calibration, transforms, quality flags. Full reproducibility for research & deployment.

``` ## How It Works A four-stage pipeline takes any vendor-specific tactile capture and emits a standardized **Tensiq Tensor (TT)** — ready for control loops, ML training, or long-horizon foundation models. ```{raw} html
// INGEST // ADAPT // COMPUTE // EMIT // CONSUME VISION-GEL video · timestamps PIEZORESISTIVE taxel array MULTI-ZONE HAND N contact regions CUSTOM RIG via plugin SDK 1 · ADAPTER canonical feature space sensor → unified format timing align · normalize 2 · PHYSICS constitutive models • force / torque • contact / slip • sensor health • uncertainty 3 · TT EMIT Tensiq Tensor + manifest provenance · quality log audit-grade output ROBOT CONTROL real-time loop · grasping force / slip feedback ML & FOUNDATION IL · RL · diffusion tactile transformers → TT · tensor + manifest + quality.ndjson · reproducible across sensors and sessions drop-in for PyTorch dataloaders, ROS bridges, and policy training stacks
``` ## Capability Matrix ```{raw} html
Vision-gelPiezoresistiveMulti-zone handCustom rig
Ingest✓✓✓via plugin SDK
Physics outputF / T / contactF / contact / driftper-region F / contactconfigurable
Slip detection✓✓per-regionconfigurable
Quality audit✓✓✓✓
ML tokenization✓✓✓✓
``` ## Why It Matters ```{raw} html
01

One format, every sensor

No more rewriting downstream code for each new sensor vendor. The same controller, the same model, the same loader works across the catalog.

02

Physics, not pixels

Raw signal is great for research but ambiguous for control. Tensiq emits real forces, real torques, real contact — what robots actually need to act on.

03

Audit-grade by default

Every frame carries provenance. Calibration, transforms, hashes, quality flags. Pass regulatory review, reproduce a paper, deploy with confidence.

04

Built for foundation models

The output is pre-tokenized for transformers and diffusion policies. The tactile equivalent of what ImageNet was for vision — a substrate for the next generation of dexterous AI.

``` ## My Role I build the **Tensiq tokenizer** — the component that turns raw tactile-sensor frames into the discrete **Tensiq Tensor (TT)** sequences that feed multimodal foundation models. I also own the **TENSIQ CLI** end-to-end — pipeline execution, dataset tokenization, and cross-platform distribution — and lead the integration of tactile data into foundation models.