What sensors do you use?

GelSight gel sensors for high-resolution contact geometry, TouchTac tactile pads, and force sensors for normal and shear force. All mounted on UR-5 arms with parallel-jaw grippers.

How is the data collected?

Trained operators wear Meta Quest 3 headsets and teleoperate UR-5 arms in real-time. Force feedback flows back to the controller so operators modulate grip naturally. Every session is recorded at <10ms sync across all streams.

What objects are in the dataset?

50+ objects across 6 material categories: rigid household (YCB-aligned), deformable, textured surfaces, granular/filled, tools & handles, and fragile/thin. Each object is chosen to elicit specific tactile signals.

What annotation do you provide?

Professional annotators label every episode: natural language task description, grasp outcome (success/partial/fail), object ID, material class, grip type, and force profile tags. Delivered in MCAP with per-frame labels.

What models does it work with?

Compatible with OpenVLA, LeRobot, and custom PyTorch/JAX dataloaders. MCAP format with NL annotations. No preprocessing required.

How do I get started?

Email hello@physicalaidata.co or fill out the contact form. We’ll scope a pilot around your target objects and manipulation tasks, collect data, annotate, and deliver within weeks.

Data Dashboard | Physical AI Data

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