可変剛性グリッパの把持空間をVAEで学習する
Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
少数の専門家把持データから変分オートエンコーダで把持空間をコンパクトにモデル化し、未学習のグリッパ構成を生成して把持空間を探索する手法を提案した。
著者: Clément Rolinat, Mathieu Grossard, Saifeddine Aloui, Christelle Godin
分類: cs.RO
原文アブストラクト
Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.
