phydata
Research

Coverage before scale: mapping the long tail of real work

phydata ResearchField systems

Physical work is full of low-frequency decisions that determine whether a task succeeds. These moments are easy to miss when a data program measures only hours, clips, or frames.

We treat coverage as a map of conditions and outcomes. The goal is to know which parts of the task space are represented, which are thin, and which are still unknown.

Build the map first

A coverage map begins with the axes that change execution. For one task that might be object geometry, surface condition, tool wear, and operator strategy. For another it might be anatomy, access angle, sensing quality, and recovery action.

Figure 1. This example makes the long tail visible: common conditions are well represented while recovery cases remain scarce.

Collect where uncertainty is highest

Once the map exists, collection can target thin regions instead of repeating the easiest demonstrations. Field teams receive specific requests, and every new trace can be evaluated against a known gap.

Figure 2. The decision rule directs the next collection dollar toward thin, valuable task regions.

Failures are part of the task

Recovery behavior often contains the strongest supervision in physical data. A useful dataset includes failed attempts, safe stops, corrections, and the signals that helped an operator decide what to do next.

Figure 3. A complete dataset retains how operators stop, correct, and verify recovery, not only successful attempts.

Figures use illustrative values.

Tell us the capability your model is missing.

Describe the task, the setting, and what success looks like. We design the collection around it and deliver the data in your schema.