Foundation Models

Hallucination

Hallucination is the tendency of large generative models to produce fluent but factually incorrect or ungrounded outputs, such as referencing objects that are not present in a scene. In robotics, hallucination appears when language-model planners propose infeasible actions or when vision-language-action models misreport scene contents, producing confident commands that do not correspond to physical reality.

Why it matters for physical AI

A hallucinated plan step can command a robot to grasp a nonexistent object or violate safety constraints, so grounding model outputs in perception and feasibility checks is a core requirement for deploying foundation models on hardware.