Demo: Task Cooperation for Urban Unmanned Sanitation Vehicles
Bibliographic record
Abstract
Unmanned sanitation vehicles (USVs) promise cleaner cities, yet efficiently coordinating multiple USVs in large urban areas remains challenging due to constraints such as limited waste capacity and battery life. In this demo, we present MRTC, a multi-robot task cooperation system. First, Dynamic Task Assignment employs an Actor-Critic policy within a Markov decision framework to allocate cleaning tasks and decide the required number of USVs. Second, Single-USV Path Planning refines each route via a fast two-layer iterative search. Over an eight-month real-world deployment in three urban testbeds, our MRTC system markedly improved cleaning efficiency while lowering operating costs. Operating over a combined 10,775 km of routes per month, the system achieved average monthly savings of 20,575 kWh of energy and 2,744 labour hours. A demonstration video is available at https://llq978.github.io/Demo/.
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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 | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".