視覚的タスク計画における再構成ベースと対照学習ベースのモデル比較
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
画像からの状態表現学習において、再構成損失と対照損失を比較し、タスク事前知識を活用する対照学習モデルが視覚的タスク計画で優れることを示した研究。
著者: Constantinos Chamzas, Martina Lippi, Michael C. Welle, Anastasia Varava, Lydia E. Kavraki, Danica Kragic
分類: cs.RO, cs.LG
原文アブストラクト
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is often assumed and used as a proxy for estimating similarity between the underlying states of the system. However, observations commonly contain task-irrelevant factors of variation which are nonetheless important for reconstruction, such as varying lighting and different camera viewpoints. In this work, we define relevant evaluation metrics and perform a thorough study of different loss functions for state representation learning. We show that models exploiting task priors, such as Siamese networks with a simple contrastive loss, outperform reconstruction-based representations in visual task planning.
