Every public dataset is vision-only demonstrations of things going right. Pisikal collects what the hand actually feels — and the failures, slips, and edge cases nobody else records.
A policy trained only on cameras has no signal for the moment that decides every manipulation task: contact. Whether the grasp holds, whether the object is slipping, how much force a soft thing can take. That information lives in the fingertips — and it's missing from almost every robotics dataset on earth.
Geometry, color, pose, motion. Enough to plan a reach. Blind the instant the gripper closes.
The contact event itself, captured as data: pressure fields, shear, slip onset, and the failures that teach robustness.
Nominal demos teach the easy 80%. The slip, the clutter, the occluded grab, the object that deforms — that's the 20% that breaks deployed policies.
And it's exactly the data nobody collects, because it's hard to stage and harder to label. We collect it on purpose: structured failure, edge cases, and recoveries, mapped to a taxonomy your team can train against.
Tactile is the foundation. Edge cases are the proof. Custom is how it arrives shaped to your robot, your gripper, your task.
Pressure fields, normal and shear force, slip onset, and surface texture — captured at the fingertip with GelSight-class and force-torque sensing. The contact event as data.
Slips, occlusions, clutter, deformables, bad lighting, and the recoveries that follow. Staged on purpose and labeled to a taxonomy — the tail your policy never saw.
Your embodiment, your gripper, your objects, your environment. We collect to your spec and annotation schema so the data drops straight into your training pipeline.
We're early — and direct about it. Here's the collection spec we're building design partners around. Modalities, taxonomy, and schema are co-defined with our first partners, not handed down.
Early access: We're onboarding a small group of design partners to co-define modalities and taxonomy against real training needs — and to be first in line for the data.
There's no ImageNet for physical AI — and when it arrives, it won't be more demos. It'll be touch, and the failures.
Vision and language got their data flywheel a decade ago. Manipulation never did. The field is scaling demonstrations, but demonstrations of success in clean conditions don't transfer to messy rooms and fragile objects.
The bottleneck isn't model capacity anymore — it's the absence of contact-rich, failure-aware data. That's the gap Pisikal exists to fill, collected to your task instead of scraped from whatever happened to be lying around.
// Pisikal — physical AI training data
Tell us your embodiment and the tasks that keep breaking. We'll scope a tactile and edge-case collection around them.
Book a call →Now onboarding design partners · labs & robotics teams