Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916691210928128 |
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| author | Ahn, Hyojun Oh, Seungcheol Kim, Gyu Seon Jung, Soyi Park, Soohyun Kim, Joongheon |
| author_facet | Ahn, Hyojun Oh, Seungcheol Kim, Gyu Seon Jung, Soyi Park, Soohyun Kim, Joongheon |
| contents | This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control Ahn, Hyojun Oh, Seungcheol Kim, Gyu Seon Jung, Soyi Park, Soohyun Kim, Joongheon Artificial Intelligence Robotics 68T05 This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics. |
| title | Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control |
| topic | Artificial Intelligence Robotics 68T05 |
| url | https://arxiv.org/abs/2504.10831 |