Physical AI has a brain.
It does not have enough senses.
Platform
The Data Layer for Embodied AI
Beyond video
Vision alone fails at micro-dexterity. Contact occlusion, low-light reflectance, and sub-millimeter joint displacements are invisible to cameras. Without high-fidelity physical sensing, embodied AI trains on an incomplete picture of the real world.
Physical sensing, not pixels
Our EMF-based sensor arrays and force transducers capture joint-angle telemetry at 1 kHz, contact forces at 500 Hz, and surface interaction data that cameras miss. The result: training datasets that represent the full physics of manipulation.
Pipeline
3-step pipeline
From raw sensor streams to training-ready datasets. ROS2-native capture, automated curation, and framework-agnostic export.
Capture
Stream 100 Hz joint-angle and force telemetry from your fleet over ROS2. Raw data lands in our edge buffer with hardware-validated timestamps.
ROS2 · 1 kHzCurate
Segment, label, and compress into HDF5 or Zarr archives. Automatic occlusion fill, contact-force alignment, and episode extraction.
HDF5 / ZarrTrain & Export
Push PyTorch-ready dataloaders directly into your training pipeline. Export to ONNX or TensorRT for on-robot inference at deployment.
PyTorch · ONNXLive Preview
Real-time telemetry stream
Simulated 100 Hz force and kinematic streams. The same data your fleet sends to our pipeline every millisecond.
Enterprise
Enterprise & Fleet Package
Deploy at scale with hardware-backed fleet packages for 50 to 200+ pairs. Dedicated on-prem infrastructure, priority SDK support, and cloud calibration SaaS that keeps every unit within spec across your entire fleet. Current partner labs include Physical Intelligence, Figure, Tesla, Skild, and Apptronik.