As the number of cases in a case-base reasoning system increases, both storage cost and retrieval time also increase. Case-base maintenance mitigates this problem by deleting cases, and various orderings of the cases for deletion attempt to minimize the loss of problem solving competence. Existing methods assume (1) that the cases have a uniform size and (2) that the case contents are indivisible. In contrast to assumption (1), exible feature deletion balances the varying storage costs of di erent cases with their competence bene ts. And for assumption (2), exible feature deletion subdivides selected cases by deleting or compressing their features or subcomponents. Experimental results supported that, for suitable cases bases, exible feature deletion can outperform existing per-case strategies. In this companion video, a live-action, life-size CBR robot demonstrates each of the four phases of the CBR cycle: retrieve, reuse, revise, and retain. And a teacher explains exible feature deletion while the robot performs maintenance on articles of his clothing.