Section 145 · Chapter 18, Embodied and Long-Running AI Systems
Embodied Robotics: Safety in Real-World Environments
Robots turn AI failures into motion, force, contact, and consequence. Real-world testing starts by making the physical risk visible.
What to do
- Start by mapping the real environment.
- Score task completion only after scoring unsafe passes.
- Use simulation for coverage, hardware-in-the-loop for integration, and staged physical trials for reality.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for embodied robotics safety real environments needed to reproduce work on Embodied Robotics: Safety in Real-World Environments.
- Report results for embodied robotics safety real environments by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The deeper move is to combine hazard analysis, fault tree analysis, operational design domains, safety envelopes, physical interlocks, human factors, and near-miss telemetry. Use simulation for coverage, hardware-in-the-loop for integration, and staged physical trials for reality. A robot eval that only reports task success is missing the main thing.
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Cite this page
Jason Arbon. "Embodied Robotics: Safety in Real-World Environments." Testing AI Knowledge Edition, section 145.
https://jarbon.ai/testing-ai/knowledge/ch145-embodied-robotics-safety-real-environments.html