物体アフォーダンスを活用した把持タイプ認識
Grasp-type Recognition Leveraging Object Affordance
RGB画像と対象物体名から把持タイプを認識する際、物体ごとの把持タイプ事前分布（アフォーダンス）でCNNの認識結果を補正し、精度を向上させた研究。
著者: Naoki Wake, Kazuhiro Sasabuchi, Katsushi Ikeuchi
分類: cs.RO, cs.CV
原文アブストラクト
A key challenge in robot teaching is grasp-type recognition with a single RGB image and a target object name. Here, we propose a simple yet effective pipeline to enhance learning-based recognition by leveraging a prior distribution of grasp types for each object. In the pipeline, a convolutional neural network (CNN) recognizes the grasp type from an RGB image. The recognition result is further corrected using the prior distribution (i.e., affordance), which is associated with the target object name. Experimental results showed that the proposed method outperforms both a CNN-only and an affordance-only method. The results highlight the effectiveness of linguistically-driven object affordance for enhancing grasp-type recognition in robot teaching.
