Section 150 · Chapter 18, Embodied and Long-Running AI Systems
Embodied Robotics: Sensor Fusion, Perception, and World Models
Robots fail when the world they think they see is not the world they are actually in.
sensor fusionworld modelembodied robotics sensor fusion models
What to do
- Test perception as a stack, not a single model.
- Use calibrated confidence, disagreement detection, sensor ablation, robustness tests, adversarial physical examples, and replayable sensor logs.
- Define runnable checks that exercise sensor fusion, world model, and embodied robotics sensor fusion models.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for sensor fusion, world model, embodied robotics sensor fusion models needed to reproduce work on Embodied Robotics: Sensor Fusion, Perception, and World Models.
- Report results for sensor fusion, world model, embodied robotics sensor fusion models by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Separate perception metrics from task metrics. Use calibrated confidence, disagreement detection, sensor ablation, robustness tests, adversarial physical examples, and replayable sensor logs. The most useful bug report often starts with "the robot believed the world looked like this."
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Embodied Robotics: Sensor Fusion, Perception, and World Models." Testing AI Knowledge Edition, section 150.
https://jarbon.ai/testing-ai/knowledge/ch150-embodied-robotics-sensor-fusion-models.html