Learning to cooperate together: A semi-autonomous control architecture for multi-robot teams in urban search and rescue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it