Finishing the Chore Is Not the Same as Doing It Your Way

A household robot can put an object in the right place and still ignore how its user wanted the task done. LIBERO-PeRM turns that distinction into a controlled benchmark spanning motion, skill and user-profile preferences across five suites and 312 tasks. The authors test zero-shot following, learning from demonstrations, data efficiency, generalization, composition and conflict handling. Current vision-language-action policies struggled with zero-shot personalization, while supervised fine-tuning made preferences learnable. Mixed training preserved more of the no-preference behavior than sequential adaptation and benefited from contextual prompts, but it could weaken motion-preference learning; generalization and conflicting preferences remained difficult. The benchmark is simulated and limited to language-expressible preferences that can be evaluated automatically, so it does not establish safe household deployment. Its useful contribution is to stop counting mere completion as proof that a robot served the person using it.