Robot Learning

Partial Observability

Partial observability is the condition in which a robot's sensors do not reveal the full state of the environment, formalized by the partially observable Markov decision process (POMDP). Occlusions, limited fields of view, noisy sensing, and hidden object properties such as mass or friction all induce it. Agents cope by maintaining belief distributions, filtering state estimates, or conditioning policies on observation histories via recurrence or attention.

Why it matters for physical AI

Real-world robotics is almost never fully observable: contact forces, occluded geometry, and object dynamics must be inferred over time, which is why memory-equipped policies often outperform purely reactive ones.