data

Egocentric and spatial data, captured across real factory and retail environments, structured for how Physical AI teams actually use it.

Egocentric data: First-person, task-level video

Captured via head- or body-worn cameras on real factory and retail workers performing
their actual jobs — not staged demonstrations. This preserves the signals third-person footage
loses: hand position, object contact points, natural gaze behavior, and the fine motor
sequences involved in tool use and manipulation.
Use cases:
1. Imitation learning and visuomotor policy training
2. Manipulation and tool-use benchmarking
3. Human-to-robot skill transfer
4. Foundation model pretraining at scale

Spatial / SLAM data: Real indoor environments, mapped and navigated

Retail floors and factory lines are exactly the kind of space SLAM struggles to get good data from: dynamic, cluttered, GPS-denied, and usually private property. We already have camera and wireless-positioning infrastructure live in these spaces — which means real navigation-relevant data from environments most providers can only simulate.

Use cases:
1. Indoor navigation and localization for service and mobile robots
2. Fleet-scale autonomous mobile robot (AMR) training for warehousing and logistics
3. SLAM algorithm benchmarking in real, dynamic conditions
4. Sim-to-real gap closing for indoor deployment

Sensor fusion (in progress): Combining Computer Vision & Wireless sensors' data

Our retail analytics partner network already fuses camera and wireless-positioning data
for footfall and heatmap analytics. We’re extending that same fused pipeline to produce multimodal datasets for robotics — camera plus positioning data captured from the same physical
space, at the same time.

Infrastructure first. Data second: Most data providers are building the physical layer from zero. We're repointing infrastructure that already exists.

The infrastructure advantage:

Camera-based retail analytics — footfall, heatmaps, customer journey — is already running across 4,000+ stores through our partner network. Wireless people-counting sensors are live in 500 of those. We have 1000+ cars in fleets.

That’s not a plan for future deployment; it’s operating infrastructure we’re extending to serve a second purpose: structured training data for Physical AI.

Alongside it, we have an operations network built for running large-scale physical rollouts —
the kind of “feet on the street” capability that turns a data-collection plan into thousands of
actual capture sessions

Variation is 'sine qua non' for good training data

Most egocentric datasets available today come from a narrow set of environments —
homes, kitchens, or a small number of factories concentrated in one region.

Physical AI teams training general-purpose models need more variation than that: different facility layouts,
different tasks, different populations, different lighting and conditions.
India’s manufacturing and organized retail sector offers scale and diversity that’s
underrepresented in most existing datasets — and we’re building the pipeline to turn that into
usable, structured training data.

Stop training on incomplete signals

VoltQ helps Physical AI and robotics companies access real-world, multimodal human and industrial environments for training and evaluating embodied AI.

Physical AI Infrastructure & Robotics Data