Physical AI infrastructure and open-source stack map
Map the software layers needed for physical AI and robotics infrastructure.
Relevant capabilities
- Projects grouped by infrastructure layer
- Official project references
- Connections to data and AI infrastructure
How should you compare the options?
- Map ingestion, storage, training, simulation, serving and observability as separate infrastructure layers.
- Hardware compatibility, safety requirements and deployment environment constrain the software choice.
- Treat the stack map as a planning aid; confirm project maturity, licenses and operational support directly.
What should you bring?
- Sensor/data sources and intended deployment environment
- Hardware, models and integration requirements
- Safety, latency, data retention and operational ownership constraints
What does a practical first workflow look like?
- Map the target workflow to the infrastructure layers.
- Review official project references and identify missing integrations.
- Test a bounded non-production pipeline on representative data.
- Agree deployment scope and specialist responsibilities before production use.
What should you check before expanding?
- Validate data formats and hardware compatibility.
- Measure pipeline latency and recoverability.
- Confirm domain-specific safety review and operating ownership.
How should you evaluate the cost?
Evaluate infrastructure capacity and the work required to operate the selected components. Open-source licensing alone does not determine total cost.
What needs checking before switching?
Check hardware, data formats, integration needs and operational ownership for your specific deployment.