Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
M. Asif Rana, Mustafa Mukadam, S. Reza Ahmadzadeh, Sonia Chernova, Byron Boots
Abstract
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.
BibTeX
@inproceedings{iros2018_learninggenerali,
title = {Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments},
author = {M. Asif Rana and Mustafa Mukadam and S. Reza Ahmadzadeh and Sonia Chernova and Byron Boots},
booktitle = {IROS 2018},
year = {2018}
}