センサなし人間動作からの物体操作タスク学習：主要課題の整理
Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges?
RGB/RGB-Dカメラのみで人間の実演から物体操作を学習する際の課題を、6DoF姿勢推定や言語処理を含めて整理し、接着剤塗布とブロック積みの事例で検討した。
著者: Radoslav Skoviera, Karla Stepanova, Michael Tesar, Gabriela Sejnova, Jiri Sedlar, Michal Vavrecka, Robert Babuska, Josef Sivic
分類: cs.HC, cs.RO
原文アブストラクト
In this paper, we consider the problem of learning object manipulation tasks from human demonstration using RGB or RGB-D cameras. We highlight the key challenges in capturing sufficiently good data with no tracking devices - starting from sensor selection and accurate 6DoF pose estimation to natural language processing. In particular, we focus on two showcases: gluing task with a glue gun and simple block-stacking with variable blocks. Furthermore, we discuss how a linguistic description of the task could help to improve the accuracy of task description. We also present the whole architecture of our transfer of the imitated task to the simulated and real robot environment.
