骨格特徴補正による身体差異を考慮した模倣学習
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
実演者と学習者の骨格特徴の違いを学習済みアフィン変換で補正し、身体構造が異なる場合でも模倣学習を可能にする手法SILEMを提案。ヒューマノイドの歩行学習などで有効性を示した。
著者: Eddy Hudson, Garrett Warnell, Faraz Torabi, Peter Stone
分類: cs.LG, cs.RO
原文アブストラクト
Learning from demonstrations in the wild (e.g. YouTube videos) is a tantalizing goal in imitation learning. However, for this goal to be achieved, imitation learning algorithms must deal with the fact that the demonstrators and learners may have bodies that differ from one another. This condition -- "embodiment mismatch" -- is ignored by many recent imitation learning algorithms. Our proposed imitation learning technique, SILEM (\textbf{S}keletal feature compensation for \textbf{I}mitation \textbf{L}earning with \textbf{E}mbodiment \textbf{M}ismatch), addresses a particular type of embodiment mismatch by introducing a learned affine transform to compensate for differences in the skeletal features obtained from the learner and expert. We create toy domains based on PyBullet's HalfCheetah and Ant to assess SILEM's benefits for this type of embodiment mismatch. We also provide qualitative and quantitative results on more realistic problems -- teaching simulated humanoid agents, including Atlas from Boston Dynamics, to walk by observing human demonstrations.
