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Robotic Control Systems for Physical Environments

7 chapters · 32 lessons

1. 1. Sense-Decide-Act Architecture for Physical Systems

   5 lessons

   Understand the core control loop that enables autonomous systems to perceive, reason, and act in real environments.

2. 2. Operational Design Domains and Safety Constraints

   4 lessons

   Define where and when your autonomous system can operate safely and communicate those boundaries clearly.

3. 3. Fleet Learning and Edge Case Capture

   5 lessons

   Build data pipelines that identify and prioritize rare, safety-critical scenarios for model improvement.

4. 4. Safety Monitoring and Runtime Assurance

   4 lessons

   Implement real-time checks and fallback systems that prevent unsafe actions before they occur.

5. 5. Perception System Design and Sensor Fusion

   5 lessons

   Combine multiple sensor streams into reliable world models that handle occlusion, noise, and ambiguity.

6. 6. Continuous Improvement and Model Updates

   4 lessons

   Deploy software updates safely to fleets while measuring real-world performance and preventing regressions.

7. 7. Application Domains and System Tradeoffs

   5 lessons

   Adapt control system design to different operating environments from highways to warehouses to open roads.