Telepath data programs capture robot interactions in active facilities so teams get real variance, true edge cases, and task context that staged datasets often miss. Instead of one-time recording sessions, collection runs inside recurring production workflows where object state, timing pressure, and environmental conditions naturally vary. The result is a dataset profile that better matches deployment reality and supports stronger autonomy generalization.
Capture behavior where robots and operators already execute real business tasks. This includes irregular item presentation, non-ideal placements, and interruption patterns that rarely appear in controlled environments.
Use structured capture standards across facilities and task categories. Collection specs, QA thresholds, and acceptance criteria are defined before launch to keep data quality stable across batches.
Deliver data in formats that model and infrastructure teams can use directly. Programs can align to schema conventions required by your training, evaluation, and replay workflows.
Map collection volume and scenario mix to concrete capability targets, so every batch is tied to a measurable model-development objective.
Data comes from working environments with natural interruptions, time pressure, and variable object states. That gives model teams access to the long-tail scenarios that usually drive field failures after launch.
Programs define collection scope, QA criteria, and delivery standards before launch to reduce downstream cleanup costs. This avoids high-volume capture that later becomes expensive to normalize or discard.
Collection goals map to concrete autonomy or policy benchmarks so teams can measure progress against useful targets. As performance improves, objectives can shift toward the next set of failure modes and edge cases.
Delivery plans can be synchronized with training sprints so new production traces are folded into evaluation and retraining cycles while operational context is still current.
Production data includes real variability, interruptions, and edge cases that better reflect deployment conditions than controlled lab environments. It captures recovery behavior, imperfect object states, and temporal variation across shifts, which are frequently missing from lab collections.
Yes. Programs can be scoped around specific task families, benchmark goals, and delivery requirements. Teams can define which capabilities matter most, what quality thresholds are required, and how data should be packaged for downstream training.
Delivery cadence is flexible and can be configured for recurring drops aligned with your model development cycle. Most teams run weekly or bi-weekly delivery loops so new data can be integrated quickly into policy iteration and evaluation.
Yes. Programs can expand across facilities or adjacent workflow classes once baseline quality is stable. This lets teams increase distribution coverage while preserving consistent schema and QA standards.