正則化メトリック学習によるアフォーダンス理解の汎化のための不変特徴マッピング
Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning
同じアフォーダンスを持つ物体を特徴空間で近づけるメトリック学習を正則化し、無関係な特徴を罰することで、感覚入力のどの部分がアフォーダンスを生むかを特定し、アフォーダンス間の類似性推論を可能にした研究。
著者: Martin Hjelm, Carl Henrik Ek, Renaud Detry, Danica Kragic
分類: cs.RO
原文アブストラクト
This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding of affordances is finding sensory-grounded semantics. Being able to understand what in the representation of an object makes the object afford an action opens up for more efficient manipulation, interchange of objects that visually might not be similar, transfer learning, and robot to human communication. Our approach uses a metric learning algorithm that learns a feature transform that encourages objects that affords the same action to be close in the feature space. We regularize the learning, such that we penalize irrelevant features, allowing the agent to link what in the sensory input caused the object to afford the action. From this, we show how the agent can abstract the affordance and reason about the similarity between different affordances.
