@PrismaXai launches VLA Foundry—a decentralized end-to-end infrastructure specifically designed for producing large-scale Vision-Language-Action (VLA)-based robotics datasets. The VLA Foundry's working system rests on three main architectural pillars:

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Breaking down the @PrismaXai Verify quality workflow 🧵

6.Task List A queue of robotics datasets awaiting review, including tasks such as Replenish the Refrigerator, Prepare Clothes for the Trip, and many others.

Rating Scale: Poor → Weak → OK → Good → Excellent

•Task completion speed → Measures the efficiency and speed of task execution. •Task fully completed → Confirms that every step of the task was completed successfully.

5. Quality Assessment Evaluates the overall execution quality of the robot. •Robot control quality → Assesses the precision and stability of the robot's control. •Movement smoothness → Evaluates how smooth and natural the robot's movements are.

•Robot hand stays in frame → Ensures the robot's hand remains visible throughout the recording. •All cameras in sync → Confirms that all camera views are synchronized and recorded simultaneously.

•Clear camera feed → Confirms that all camera footage is clear and visible. •Task completed as instructed → Verifies that the robot completed the assigned task correctly.

4. PASS / FAIL Criteria Used to determine whether a dataset is suitable for AI training.

3. History Displays a complete record of previously submitted reviews, including review status and account activity.

2. Review & Earn The workspace where contributors select available tasks, review robotics datasets, submit evaluations, and earn points or rewards for their contributions.

1. Data Quality A quality assurance system that ensures every robotics dataset meets the required standards before being used to train AI models.

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