ベイズニューラルネットワークの平滑化予測によるロボットの内省的知覚
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
ベイズNNの不確実性推定を改善し、CRFの単項ポテンシャルや半教師ありドメイン適応に活用してロボットの物体認識精度を向上させた研究。
著者: Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton, Ferenc Balint-Benczedi, Rudolph Triebel
分類: cs.RO, cs.AI
原文アブストラクト
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts in annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks.
