Robot Learning

Out-of-Distribution (OOD)

Out-of-distribution (OOD) describes inputs or situations that differ statistically from a model's training data, such as novel objects, lighting, backgrounds, or states reached after compounding errors. Model behavior on OOD inputs is unreliable and often confidently wrong, motivating OOD detection, uncertainty estimation, and data collection strategies that deliberately widen coverage.

Why it matters for physical AI

The physical world guarantees encounters outside any training set, so detecting distribution shift and failing safely, rather than acting confidently on garbage, is core to deployable robot autonomy.