DARTによる深層模倣学習を用いたロバストなベッドメイキングの学習
Learning Robust Bed Making using Deep Imitation Learning with DART
トヨタHSRでシーツ把持と失敗検出の人間デモンストレーションからYOLO特徴を用いた深層方策を学習し、マーカーなしでベッドメイキングを自動化した。50デモで96%のシーツ被覆率を達成し、輪郭検出ベースラインを大幅に上回った。
著者: Michael Laskey, Chris Powers, Ruta Joshi, Arshan Poursohi, Ken Goldberg
分類: cs.RO
原文アブストラクト
Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception in an unstructured environments, deformable object manipulation, obstacle avoidance and sequential decision making. We explore how DART, an LfD algorithm for learning robust policies, can be applied to automating bed making without fiducial markers with a Toyota Human Support Robot (HSR). By gathering human demonstrations for grasping the sheet and failure detection, we can learn deep neural network policies that leverage pre-trained YOLO features to automate the task. Experiments with a 1/2 scale twin bed and distractors placed on the bed, suggest policies learned on 50 demonstrations with DART achieve 96% sheet coverage, which is over 200% better than a corner detector baseline using contour detection.
