Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidance

Understand

While Vision-Language-Action (VLA) models show strong generalizability in various tasks, real-world deployment of robotic policy still requires large-scale, high-quality human expert demonstrations.

• However, data collection via human teleoperation requires continuous operator attention, which is costly, hard to scale.
• To address this, we propose Genie Centurion (GCENT), a scalable and general data collection paradigm based on human rewind-and-refine guidance, enabling robots' interactive learning in deployment.
• GCENT starts at an imperfect policy and improves over time.