在边缘设备上用本地模型自动生成机械电子系统报告
Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems
- 用本地部署的多模态模型生成自然语言报告
- 跨室内、室外、城市环境验证，支持隐私保护
- 适合机器人评估与智能系统部署场景
深度学习的进步使硬件集成的人工智能系统（如机器人）能够与动态非结构化环境交互。然而，在自动驾驶、服务与护理机器人等关键任务中，需评估大量异构数据。自动报告生成对系统评估与应用推广至关重要。本文提出一种仅依赖边缘计算设备上本地模型的自动化报告生成流水线，利用多种多模态传感器生成自然语言报告，保障所有参与者隐私，无需外部服务。我们在涵盖室内、室外及城市环境的多样化数据集上进行了评估，提供定量与定性结果。示例报告及其他补充材料已公开于公共仓库。
原文摘要 · Abstract (English)
Recent advancements in Deep Learning enable hardware-based cognitive systems, that is, mechatronic systems in general and robotics in particular with integrated Artificial Intelligence, to interact with dynamic and unstructured environments. While the results are impressive, the application of such systems to critical tasks like autonomous driving as well as service and care robotics necessitate the evaluation of large amount of heterogeneous data. Automated report generation for Mobile Robotics can play a crucial role in facilitating the evaluation and acceptance of such systems in various domains. In this paper, we propose a pipeline for generating automated reports in natural language utilizing various multi-modal sensors that solely relies on local models capable of being deployed on edge computing devices, thus preserving the privacy of all actors involved and eliminating the need for external services. In particular, we evaluate our implementation on a diverse dataset spanning multiple domains including indoor, outdoor and urban environments, providing quantitative as well as qualitative evaluation results. Various generated example reports and other supplementary materials are available via a public repository.
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