情報経路計画による意味セグメンテーションのための身体性能動ドメイン適応
Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning
屋内環境で意味セグメンテーションモデルを自己教師ありドメイン適応させるため、モデルの不確実性を利用した情報獲得量に基づく経路計画でデータを収集する自律エージェントを提案した論文。
著者: René Zurbrügg, Hermann Blum, Cesar Cadena, Roland Siegwart, Lukas Schmid
分類: cs.RO
原文アブストラクト
This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.
