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Test Distribution Design for Embodied AI Systems

7 chapters · 31 lessons

1. 1. Coverage vs Volume in Physical AI Systems

   4 lessons

   Understand why data diversity drives generalization in embodied systems more than raw data volume.

2. 2. Defining and Measuring Distribution Coverage

   5 lessons

   Learn frameworks and metrics to quantify how well training data covers deployment conditions.

3. 3. Designing Test Sets for Zero-Shot Evaluation

   5 lessons

   Build test distributions that predict performance on unseen environments, objects, and conditions.

4. 4. Evaluating Representations and Policies Separately

   4 lessons

   Isolate whether generalization failures stem from perception, planning, or control components.

5. 5. Systematic Data Collection for Coverage

   5 lessons

   Apply structured methods to expand training diversity without exponential cost.

6. 6. Communicating Distribution Risk to Stakeholders

   4 lessons

   Translate coverage analysis into actionable insights for product, business, and safety teams.

7. 7. Continuous Evaluation and Distribution Monitoring

   4 lessons

   Build systems to track distribution shift and coverage degradation in production.