Sensor & IMU → Robotics manipulation
Sensor & IMU for Robotics manipulation
Robotics manipulation needs egocentric and third-person demonstrations with pose and contact detail. Here is why Sensor & IMU is the right raw material for it, what buyers typically spec, and how the rights are handled.
Why Sensor & IMU for Robotics manipulation
Robotics teams buy IMU data for two jobs. First, proprioception modelling: inertial streams from bodies in motion — human or device — ground how movement feels from the inside, complementing vision the way inner ear complements eyes. Paired with egocentric video or mocap, IMU gives world-model and control pipelines an action-adjacent channel that pixels alone lack, and it is the cheap sensor that survives darkness, occlusion, and motion blur. Second, dynamics coverage: contact events, impacts, vibration, and gait signatures live at frequencies video cannot resolve, so high-rate inertial capture (100–200 Hz and up) during real manipulation and locomotion is the honest record of those dynamics. The spec that matters most is synchronization — an IMU stream is only as useful as the timebase tying it to the video, mocap, or robot log it accompanies.
What buyers typically spec
Industry-typical ranges — a brief can and should deviate where the task demands it.
| Typical volume | Hundreds of hours of task-relevant motion; synchronized multimodal sessions prioritized |
|---|---|
| Signals | 6–9-axis IMU at 100–200+ Hz; multiple mount points where specced |
| Sync | Hardware or verified software sync to video/mocap; residual offset reported |
| Labels | Task phases, contact/impact events, carried-object tags |
| Formats | CSV/JSON streams; MP4 where video-paired |
A sample brief
The shape of a workable request — swap in your own numbers and conditions:
- Modality: wrist + chest IMU synchronized with head-mounted video during manipulation tasks.
- Volume: 150 hours across 50 participants and 20 task types.
- Labels: task phases and contact events; sync residual documented per session.
- Rights: participant consent naming commercial AI training.
fiund's sourcing angle
Sensor data is abundant on devices but almost never licensed cleanly. fiund sources consented streams to spec. We source to a brief and clear the rights before anything moves, so what you receive is both useful and defensible in diligence.
Rights posture
Signed licence, explicit training rights, separate voice/likeness consent, nothing scraped. See the rights & provenance guides.
Frequently asked questions
What sampling rate does dynamics work need?
Contact and impact events concentrate energy at high frequencies — 100–200 Hz is the working floor, higher for percussive tasks. Coarse activity work tolerates 50 Hz, but you cannot recover an impact profile that was never sampled.
How tight does IMU-video sync need to be?
Tight enough that events align at your training resolution — frame-level for 30 fps video, better for contact modelling. Hardware sync at collection is the reliable route; a corpus should report its measured residual, not assert "synchronized".
Other data for Robotics manipulation
More Sensor & IMU use cases
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