Beyond End-Task Success: How to Audit Visual Experience Retrieval in Robotics
Robots that store past experiences must select which one to reuse in a new scene. Most systems select by visual similarity, and most evaluations report only the success of the selected experience. That number does not show whether the selection was good: a rule can score well by repeatedly using one broadly transferable experience, or poorly because its preferred experience is weak. Since robots increasingly adapt by reuse rather than retraining, a score that describes the library rather than the rule misleads what the field builds next. We contribute an audit methodology: execute every stored experience in every query scene, over two manipulation tasks, three reuse mechanisms, and librar...
| Authors | Eshika Pathak, Leela Krishna |
|---|---|
| Primary category | cs.RO |
| Published | 22 September 2026 |
| Last updated at source | 24 September 2026 |
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