Grid or Nodes? Probing Object Memory in World Models Under Partial Observability
A robot's world model must remember objects that leave the camera's view. FloWM, a learned world model, keeps them in a spatial grid of features that it moves with five built-in velocities. A node memory instead keeps each object's position and velocity as numbers. We compare them in FloWM's own block world, with an oracle-fed Kalman tracker as the node memory. How the grid is read changes its score: a single-cell probe finds 66% of out-of-view blocks, while FloWM's own probe 89%. FloWM also scores 8 points higher in view although no frame is shown, which we attribute to its write-back of its own predictions. Out of view, the tracker reaches FloWM's range in 14KB against 5.6MB, with no pr...
| Authors | Justin McMillen, Yasin Yilmaz |
|---|---|
| Primary category | cs.RO |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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