Summary of Maze Discovery Using Multiple Robots Via Federated Learning, by Kalpana Ranasinghe et al.
Maze Discovery using Multiple Robots via Federated Learning
by Kalpana Ranasinghe, H.P. Madushanka, Rafaela Scaciota, Sumudu Samarakoon, Mehdi Bennis
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed work showcases a practical application of federated learning (FL) in training robots equipped with LiDAR sensors to navigate mazes. The goal is to develop classification models that can accurately identify grid areas within two distinct square mazes featuring irregularly shaped walls. Due to the differing maze structures, a model trained on one maze does not generalize well to the other. FL is employed to enable robots exploring one maze to share knowledge and collectively operate accurately in the unseen maze, highlighting its effectiveness in enhancing classification accuracy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps robots find their way around mazes! The idea is to teach robots to recognize different shapes within two mazes with unique wall patterns. Because each maze has a distinct structure, a robot trained on one maze won’t be able to navigate the other. To solve this problem, scientists used FL to let robots share what they learn while exploring one maze, so they can work together to find their way around the unseen maze. This shows how FL can improve accuracy and reliability in real-world applications like maze discovery. |
Keywords
* Artificial intelligence * Classification * Federated learning
