パーティクルフィルタとロボットによる能動学習を用いた操作を通じた物体構造の学習
Object Structure from Manipulation via Particle Filter and Robot-based Active Learning
ロボットが物体を操作しながらパーティクルフィルタと能動学習で対話的に物体を領域分割し、物体構造モデルを自動的・漸進的に獲得する手法を提案した。
著者: Kun Li, Max Q. -H. Meng
分類: cs.RO
原文アブストラクト
To learn object models for robotic manipulation, unsupervised methods cannot provide accurate object structural information and supervised methods require a large amount of manually labeled training samples, thus interactive object segmentation is developed to automate object modeling. In this article, we formulate a novel dynamic process for interactive object segmentation, and develop a solution based on particle filter and active learning so that a robot can manipulate and learn object structures incrementally and automatically. We demonstrate our method with a humanoidrobot on different types of objects, and compare its segmentation performancewith established methods on selected objects. The result shows that our approach allows more accurate object modeling and reveals richer object structural information.
関連論文
- 非視覚情報を統合した確率的深度画像レジストレーション物体モデリング
