Adaptive Multi-Agent Coordination for Urban Medicine Delivery in Elderly Care Facilities
Bibliographic record
Abstract
In senior care facilities, where staff availability is limited and patient needs are timesensitive, effective and dependable medication delivery systems are essential. These issues are successfully addressed by multi-robot systems that use adaptive Multi-Agent Reinforcement Learning (MARL) to coordinate Autonomous Mobile Robots (AMRs) for on-time delivery. These systems improve operational efficiency in care facilities by enabling navigation of complex environments with both static and dynamic obstacles by utilizing powerful simulations (e.g., Webots). By integrating sophisticated MARL algorithms like Factored Centralized Actor-Critic (FACMAC) and Counterfactual Multi-Agent (COMA) into the Robot Operating System 2 (ROS 2) framework and the PyTorch library, it is possible to allocate tasks adaptively and enhance the coordination of multiple robots in real-time scenarios with varying human interactions. These algorithms improve the robots' capacity to anticipate and react to people's movements within the assisted living facility. Extensive training results show that MARLcoordinated robots achieve 92-95% completion rates with fewer collisions and improved routing efficiency, significantly outperforming classical multi-agent path finding(MAPF) systems. Insights also highlight the potential for deploying multiple physical robots and emphasize the significance of using digital twin simulations for safe training environments. The results corroborate the expanding body of research that argues for the use of intelligent robotic systems in eldercare to improve care quality and alleviate caregiver shortages. To sum up, the use of sophisticated algorithms in robotics creates the foundation for more effective and efficient drug delivery systems in senior care settings. Future research should concentrate on applying learned policies from simulation to real-world applications while maintaining ethical considerations, especially with regard to the dynamics of the relationship between elderly patients and robots.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".