宇宙探査ロボットの固有感覚データを用いた不確実性を考慮した地形分類
Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data
固有感覚データのみを入力とし、モンテカルロドロップアウトやFlipoutなどの不確実性推定を組み込んだニューラルネットワークにより、宇宙探査ロボットの地形分類を信頼性高く行う手法を提案した。
著者: Mariela De Lucas Álvarez, Jichen Guo, Raul Domínguez, Matias Valdenegro-Toro
分類: cs.RO, cs.LG
原文アブストラクト
Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can have high performance but can be deemed untrustworthy as they lack transparency, which makes them unreliable for taking high-stakes decisions during mission planning. We address this by proposing Neural Networks with Uncertainty Quantification in Terrain Classification. We enable our Neural Networks with Monte Carlo Dropout, DropConnect, and Flipout in time series-capable architectures using only proprioceptive data as input. We use Bayesian Optimization with Hyperband for efficient hyperparameter optimization to find optimal models for trustworthy terrain classification.
