Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation

Abstract:In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot reveal where sensing disturbances attenuate or persist in the pipeline. We apply 10 visual and temporal sensing degradations to a world model planner and track their effects across representation, future prediction, planner preference, and physical outcome using paired evaluation on the same 50 tasks. The relative impact of degradations was not preserved across stages: large representation shifts could attenuate downstream, while smaller initial shifts could persist to the outcome, and internal-response ordering did not directly match physical-outcome ordering. Temporal degradations also showed distinct patterns: even with similar overall changes in observation history, responses differed substantially with the location of corrupted information and the planner's actual exposure. This non-uniform stage-wise response was also observed in secondary evaluations with another manipulation task and a different world model. Stage-wise diagnosis can therefore identify where sensing disturbances attenuate or persist and help prioritize subsequent model verification and sensing mitigation.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2609.07126 [cs.RO]

(or arXiv:2609.07126v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.07126 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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