散らかった環境での実演から汎化可能なロボットスキルを学習する手法
Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
雑然とした環境での人間の実演から、不要な物体の影響を抑えつつ本質的な行動を捉える重要度重み付きバッチ・逐次スキル学習法を提案し、7自由度マニピュレータで検証した。
著者: Muhammad Asif Rana, Mustafa Mukadam, Seyed Reza Ahmadzadeh, Sonia Chernova, Byron Boots
分類: cs.RO
原文アブストラクト
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clutter-free environments. This prevents the demonstrations from being affected by irrelevant objects, whose influence can obfuscate the true intention of the human or the constraints of the desired skill. However, it is unrealistic to assume that the robot's environment can always be restructured to remove clutter when capturing human demonstrations. To contend with this problem, we develop an importance weighted batch and incremental skill learning approach, building on a recent inference-based technique for skill representation and reproduction. Our approach reduces unwanted environmental influences on the learned skill, while still capturing the salient human behavior. We provide both batch and incremental versions of our approach and validate our algorithms on a 7-DOF JACO2 manipulator with reaching and placing skills.
