This is a workflow pipeline from @axisrobotics for building datasets and training robots or physical AI. There are 5 main stages: 1. Task Generation & Asset Normalization Creating a task from a text prompt and preparing assets like 3D models so they can be used in the simulator. 2. Web-Based Teleoperation Humans control robots virtually through the web to perform tasks. Those movements are recorded as demonstration data/trajectories. 3. Offline Data Cleaning & Refinement The collected data is cleaned, for example: - Quality filtering - Hesitation Removal - Trajectory smoothing The result is refined trajectories. 4. Realistic Augmentation The cleaned data is multiplied with variations: - Visual variation - Pose variation - Noise injection - Dynamic variation The goal is for the robot to be more robust and able to generalize. 5. Policy Learning & Real-World Validation Dataset is used for policy learning, then we do sim to real transfer and test it on a robot in the real world. So this workflow is pretty important for explaining how Axis builds a data engine for Physical AI. Axis trying to build a complete pipeline from task generation all the way to the robot actually being able to perform the task in the real world. Source : 🔗https://t.co/nYmkIWawKY