Why Fleet Scale Matters

Current VLA models need 50K–500K demonstrations to generalize well. Collecting this volume on a single robot takes months or years. Fleet-scale data collection with 5–20 parallel stations can compress timelines to weeks. Google used 13 robots for RT-1 data collection; the Open X-Embodiment project aggregated data from 22 robot types across multiple labs.

Hardware Standardization

Every robot in the fleet must produce compatible data. This requires identical camera models, mounting positions, calibration procedures, gripper types, and control interfaces. Even small variations (camera angle offset of 5°) can hurt policy performance. Use a master calibration jig and verify each station against reference episodes.

Operator Pipeline

Train operators on a qualification task before production data collection. Track per-operator metrics: episodes per hour, success rate, task completion time. Rotate operators across tasks to prevent bias. SVRC offers turn-key fleet data collection services with trained operators and standardized quality pipelines.