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Building Data Pipelines for Physical AI Systems

7 chapters · 30 lessons

1. 1. Sensor Recording Architectures for Physical Systems

   4 lessons

   Design pipelines that capture multimodal data from robots operating in real environments.

2. 2. Demonstration Collection and Human-in-the-Loop Recording

   4 lessons

   Build systems that turn operator actions into structured training examples.

3. 3. Annotation Workflows for Physical AI Datasets

   5 lessons

   Add semantic meaning, safety boundaries, and success criteria to raw sensor recordings.

4. 4. Policy Training from Real-World Demonstrations

   4 lessons

   Convert annotated datasets into models that map perception to action.

5. 5. Evaluation Loops and Continuous Improvement

   4 lessons

   Test deployed policies and route failures back into training pipelines.

6. 6. Simulation Integration and Hybrid Training

   4 lessons

   Combine real-world data with synthetic environments to accelerate learning.

7. 7. Data Standards and Cross-Platform Reusability

   5 lessons

   Implement formats and metadata that make robot datasets shareable and combinable.