Learning to cooperate together: A semi-autonomous control architecture for multi-robot teams in urban search and rescue
Bibliographic record
Abstract
The goal of cooperative rescue robot teams in urban search and rescue (USAR) missions is for the rescue robots to effectively work together in order to minimize the overall exploration time it takes to search disaster scenes and find as many victims as possible. To achieve this goal, task allocation and execution amongst the team members must be considered. In this paper, a unique hierarchical reinforcement learning (HRL) based semi-autonomous control architecture is proposed for rescue robot teams to enable cooperative learning between the robot team members. The HRL-based control architecture allows a multi-robot rescue team to collectively make decisions regarding which rescue tasks need to be carried out at a given time, and which team member should execute them to achieve optimal performance in exploration and victim identification. Due to the cluttered nature of disaster scenes, we propose the development of a semi-autonomous centralized control approach to allow task sharing between the robot team members and human operators when needed. Simulation results verify the effectiveness of the proposed HRL-based methodology for multi-robot cooperative exploration and victim identification in USAR-like scenes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".