PI DATA — physical AI data, robotics datasets and robot training data for Physical
Intelligence.
We're building the data infrastructure for physical AI — real-world robotics and human egocentric data, collected, engineered and validated in India.
Six stages.
One pipeline.
Every dataset we build moves through the same pipeline — from task definition to training-ready delivery.
Define
Task + environment + embodiment
Collect
Teleoperation / autonomous / human demonstrations
Capture
RGB / depth / force / proprioception / audio
Annotate
Actions / objects / contact / events / outcomes
Validate
Calibration / QA / consistency / diversity
Deliver
Training-ready robotics datasets + APIs
What we track
Every delivery is reported against these dimensions. We publish real numbers as datasets ship — no backfilled stats.
Robot data +
human egocentric.
Two sources of physical experience, one pipeline. Egocentric human data is the highest-bandwidth source of physical experience we know; robot data grounds models in real embodiment.
Robot data
Humanoid, arm and mobile-base manipulation and movement data — teleoperated or autonomous — multimodal robot data captured with the full modality stack: RGB, depth, force/torque, joint states, tactile and audio.
Human egocentric
First-person human demonstrations of real tasks — kitchen, home, warehouse, factory — captured at scale, time-aligned and annotated. The scaling path for physical AI before, and beyond, robot fleets.
Every signal,
in sync.
High-fidelity, time-aligned sensing across both data streams.
Three ways
to work with us.
All programs are scoped to your spec — task, environment, embodiment, modality. We're early-stage: small team, direct access, fast iteration.
Robot data
Robot teleoperation and autonomous collection on arms and humanoids — robotics data collection across the full sensor stack. We run collection in India from day one.
Egocentric human
First-person human task demonstrations captured at scale — the fastest way to accumulate diverse physical experience for foundation models.
Custom dataset
You define the task, environment, embodiment and modality spec. We scope it, schedule it, collect it and deliver it training-ready.
The pipeline.
Every gate,
in writing.
Quality standards are written down before collection starts. Nothing ships without passing the same gates, every time.
Calibration
Camera intrinsics and extrinsics, force zeroing and IMU alignment checked per session.
Synchronization
All sensor streams time-aligned; drift detected and flagged before it reaches the dataset.
Annotation QA
Action, object, contact and event labels re-checked on a sampled basis against the spec.
Consistency
Trajectory validity, dropouts and sensor faults filtered out before delivery.
Diversity audit
Coverage across objects, scenes and task variants checked against what you asked for.
Delivery spec
Training-ready format, schema documentation and API access handed over with every dataset.
Where we start.
Task diversity
Households, markets, warehouses and factories — dense, varied daily-life tasks in the wild.
Collection cost
Hands-on data collection at a fraction of Western operating cost per hour.
Operator scale
A large pool of teleop operators and annotators to grow with demand.
Real environments
Noisy, uncontrolled, multilingual — exactly the conditions physical AI has to work in.
Long-term infra
Sites, tooling and pipeline built here become the first node of a global data network.
Tell us what
you need.
We work with robotics labs, humanoid companies and physical-AI teams that need real-world robot training data. Describe the task and we'll come back with a scope, a plan and a timeline. We're early-stage — you talk to the people doing the work, and we move fast.
hello@example.com