Learning Multi-agent Multi-machine Tending by Mobile Robots
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916634597261312 |
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| author | Abdalwhab, Abdalwhab Beltrame, Giovanni Kahou, Samira Ebrahimi St-Onge, David |
| author_facet | Abdalwhab, Abdalwhab Beltrame, Giovanni Kahou, Samira Ebrahimi St-Onge, David |
| contents | Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16875 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Multi-agent Multi-machine Tending by Mobile Robots Abdalwhab, Abdalwhab Beltrame, Giovanni Kahou, Samira Ebrahimi St-Onge, David Robotics Machine Learning Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions. |
| title | Learning Multi-agent Multi-machine Tending by Mobile Robots |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2408.16875 |