Task-aligned data objectives
Define collection against specific manipulation capabilities and evaluation benchmarks.
Telepath builds teleoperation datasets around concrete learning objectives, with collection programs designed for high-signal robot training data in real facilities.
Define collection against specific manipulation capabilities and evaluation benchmarks.
Capture difficult, irregular situations that improve model robustness after deployment.
Package datasets for straightforward ingestion in training and validation workflows.
Before collection starts, Telepath aligns tasks, edge cases, and success metrics with your target model behavior.
Dataset quality is improved through defined QA gates and consistent delivery criteria across every batch.
Ongoing dataset drops allow teams to iterate policies and evaluate changes on fresh, production-relevant signal.
Telepath can align delivery to your training stack and agreed schema so model teams can integrate quickly with minimal rework.
Yes. Collection programs can be built around explicit capability targets and benchmark criteria.
No. Teleoperation datasets can include humanoid and manipulator workflows depending on your data objectives.