ハードウェア制約下のナノドローンにおける視覚知覚タスクのオンデバイス自己教師あり学習
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
50g未満のナノドローン上で、事前学習済みCNNを自己教師あり学習によりオンデバイスで微調整し、ドメインシフトに対応する手法を提案。平均絶対誤差を最大30%改善し、超低消費電力SoCで22秒の微調整を実現した。
著者: Elia Cereda, Manuele Rusci, Alessandro Giusti, Daniele Palossi
分類: cs.RO, cs.AI
原文アブストラクト
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.
