RoboAuditor: Goal-Oriented Robotic System for Assessing Energy-intensive Indoor Appliance via Visual Language Models
Bibliographic record
Abstract
Energy auditing is a crucial step in building retrofitting to enhance building energy efficiency. However, auditing tasks, such as profiling energy-consuming appliances in buildings, rely heavily on human inspectors, resulting in a time- and capital-intensive process. To this end, we propose an autonomous robotic system, dubbed RoboAuditor, for identifying and localizing energy-intensive appliances in buildings given text queries from humans. RoboAuditor utilizes visual language models to predict relevance scores between text queries and observed images for goal selection in robot navigation. It then automatically identifies and localizes queried appliances while self-navigating with efficient navigational strategies. For evaluation, we deploy the proposed robotic system on a wheeled robot equipped with an RGB-D camera and run auditing tests in 12 residential buildings in 3D simulation. These buildings exhibit diverse room counts, appliance quantities, and navigable areas, and they all feature energy-intensive appliances, such as air conditioners, heaters, dishwashers, and refrigerators. We conduct two groups of experiments: the first group uses the relevance score, and the second serves as a control group without the relevance score. Results demonstrate that RoboAuditor detects queried appliances and accurately localizes their positions in buildings with an average success rate of 68.05%, showing a significant margin of 6.8% higher than the control group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".