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Learning from Demonstration (LfD) is a popular approach to endowing robotswith skills without having to program them by hand. Typically, LfD relies onhuman demonstrations in clutter-free environments. This prevents thedemonstrations from being affected by irrelevant objects, whose influence canobfuscate the true intention of the human or the constraints of the desiredskill. However, it is unrealistic to assume that the robot's environment canalways be restructured to remove clutter when capturing human demonstrations.To contend with this problem, we develop an importance weighted batch andincremental skill learning approach, building on a recent inference-basedtechnique for skill representation and reproduction. Our approach reducesunwanted environmental influences on the learned skill, while still capturingthe salient human behavior. We provide both batch and incremental versions ofour approach and validate our algorithms on a 7-DOF JACO2 manipulator withreaching and placing skills.

Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments