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Deploying AI Systems in Physical Environments

7 chapters · 33 lessons

1. 1. Perception Systems for Physical Environments

   4 lessons

   Build multi-sensor perception pipelines that convert raw signals into actionable representations under real-world constraints.

2. 2. Action Planning Under Physical Constraints

   5 lessons

   Design decision systems that balance task goals with safety, power, wear, and unpredictable human behavior.

3. 3. Fleet Operations and Remote Monitoring

   5 lessons

   Scale from single-unit demos to multi-device fleets with centralized oversight and distributed intelligence.

4. 4. Safety Frameworks and Failure Recovery

   5 lessons

   Build layered safety systems and recovery behaviors that prevent harm and restore service after exceptions.

5. 5. Continuous Learning from Operational Data

   5 lessons

   Use deployment logs, edge cases, and interventions to improve perception, planning, and fleet performance over time.

6. 6. Simulation and Testing Pipelines

   4 lessons

   Validate embodied AI systems in virtual environments before deployment and accelerate learning with synthetic data.

7. 7. Measuring Success with Operational Metrics

   5 lessons

   Shift from model accuracy to uptime, task completion, unit economics, and customer satisfaction as success criteria.