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arXiv 2403.04071cs.ROcs.AI

硬件受限纳米四旋翼飞行器上视觉感知任务的设备端自监督学习

On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors

Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒·莫勒人工智能研究所（IDSIA））；KU Leuven（鲁汶大学）；ETH Zürich（苏黎世联邦理工学院）

Elia Cereda, Manuele Rusci, Alessandro Giusti, Daniele Palossi

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AI总结：

针对硬件受限纳米无人机在未知环境中因域偏移导致感知模型性能下降的问题，提出设备端自监督微调方法，在GAP9芯片上22秒即可实现平均绝对误差最高30%的提升。

摘要：

重量低于50克的纳米无人机正受到学术界和工业界的越来越多关注。尽管存在严重的硬件限制（即处理器功耗低于100毫瓦），其最具吸引力的应用仍依赖机载深度学习模型来完成感知任务。当部署在训练数据未覆盖的未知环境中时，这些模型往往会因域偏移而表现不佳。为解决这一根本问题，我们首次提出在纳米无人机上开展设备端学习，将现场任务的第一阶段用于预训练卷积神经网络（CNN）的自监督微调。基于一个真实的视觉回归任务，我们从三个维度全面探究了微调阶段的性能-成本权衡：i）数据集规模（数据越多回归性能越好，但需要更多内存和更长计算时间）；ii）微调方法（例如微调全部模型参数与仅微调部分参数）；iii）自监督策略。我们的方法与预训练基线相比，平均绝对误差提升最高达30%，在超低功耗GWT GAP9片上系统上仅需22秒即可完成微调。通过纳米无人机上的设备端学习解决域偏移问题，不仅为硬件受限机器人领域带来了新成果，也为整个机器人学界的更广泛进展奠定了基础。

英文摘要：

Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (\ie sub-\SI{100}{\milli\watt} processor). When deployed in unknown environments not represented in the training data, these models often underperform due to domain shift. To cope with this fundamental problem, we propose, for the first time, on-device learning aboard nano-drones, where the first part of the in-field mission is dedicated to self-supervised fine-tuning of a pre-trained convolutional neural network (CNN). Leveraging a real-world vision-based regression task, we thoroughly explore performance-cost trade-offs of the fine-tuning phase along three axes: \textit{i}) dataset size (more data increases the regression performance but requires more memory and longer computation); \textit{ii}) methodologies (\eg fine-tuning all model parameters vs. only a subset); and \textit{iii}) self-supervision strategy. Our approach demonstrates an improvement in mean absolute error up to 30\% compared to the pre-trained baseline, requiring only \SI{22}{\second} fine-tuning on an ultra-low-power GWT GAP9 System-on-Chip. Addressing the domain shift problem via on-device learning aboard nano-drones not only marks a novel result for hardware-limited robots but lays the ground for more general advancements for the entire robotics community.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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