Can home robots learn how you want a task done?
A 312-task simulation benchmark separates finishing a household task from respecting a user's preference. Today's robot policies often completed the task while ignoring how the user asked it to be done.
The short version
- What happened
- LIBERO-PeRM contains 312 simulated tasks and 40,800 rollout episodes spanning motion, skill and user-profile preferences.
- Who it affects
- People with accessibility, safety or household preferences need robots to respect how a task is performed, not merely its endpoint.
- What remains unproven
- The benchmark uses simulated, predefined and language-expressible preferences. Its 15-person survey is only initial validation; no real robot or longitudinal household use was studied.
Keep in mind
The benchmark uses simulated, predefined and language-expressible preferences. Its 15-person survey is only initial validation; no real robot or longitudinal household use was studied.
Source analysis · 1 linked source. Primary analysed source date: 15 October 2026.
Read the full report · 4 min The evidence, methods, implications and limitations
Finishing a task is not the same as respecting a person
A robot can put an object in the right place and still do the job the wrong way for its user—moving too close to something fragile, grasping an item by the wrong part or ignoring which object a person prefers. A new benchmark makes that distinction measurable. Across six vision-language-action policies tested without preference-specific training, task completion often remained high while preference satisfaction was much lower.
The LIBERO-PeRM paper, submitted on 15 October and listed for NeurIPS 2026, builds 312 simulated manipulation tasks from the existing LIBERO environment. The authors define three kinds of preference: motion choices, such as avoiding an object; skill choices, such as grasping by a handle; and user-profile choices, such as selecting the item suited to a named profile. More than 100 generated trajectories per task produced 40,800 rollout episodes.[1]
The sharpest result came before fine-tuning
The benchmark scores success and preference satisfaction separately. In one illustrative result, the strongest tested base policy completed 97.5% of motion-preference tasks and 100% of skill-preference tasks, but satisfied the stated preference only 24.38% and 25% of the time. That gap is the paper's central contribution: conventional success metrics can make a personalised robot look capable when it has ignored how a person asked the task to be performed.
Supervised fine-tuning made preferences more learnable, but the training recipe created trade-offs. Mixed training retained 91% success on tasks without preferences and reached 84.55% task success across preference tasks. Sequential adaptation retained only 52.33% success on no-preference tasks. The mixed approach also handled contextual prompts better than preference-only tuning, while weakening motion-preference learning. Results fell substantially on unseen layouts.[1]
The distinction that matters
Two scores for one household task
The benchmark separates the goal from the way the user asked for it to be achieved.
Task success
Did the robot complete the physical goal?
Object or scene reaches the target state
Preference satisfaction
Did it follow the requested motion, skill or profile?
Execution matches the user's stated constraint
Conflicts reveal the missing conversation
When two learned preferences had to be combined, evaluation success and satisfaction were both 25%. In a decomposition test—inferring one preference from a combined instruction and another known preference—task success was 37.5% but preference satisfaction was zero. In task-versus-preference conflicts, 37.5% of trials completed neither side. The benchmark does not test whether a robot notices the conflict, asks for clarification or explains why it cannot comply, all of which may matter more to a household user than silent execution.
The evidence remains deliberately narrow. Every task ran in simulation; preferences were predefined, expressible in language and automatically measurable. Fifteen survey participants rated demonstrations as plausible and human-like, but there was no real robot, home or longitudinal user study. Generated demonstrations and the evaluation metrics may reflect the pipeline's own biases, and the accessible paper gives no funding or competing-interests statement. The useful next step is a real-robot study in which diverse users set, revise and resolve preferences over time—with safety failures and requests for clarification recorded alongside task completion.[1]