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@inproceedings{Rai2013-LfFD,
author = {Rai, Akshara and de Chambrier, Guillaume and Billard, A.},
title = {Learning from Failed Demonstrations in Unreliable Systems},
howpublished = { IEEE-RAS International Conference on Humanoid Robots},
year = {2013},
abstract = {This paper presents a method to teach a robot
to play Ping Pong from failed demonstrations in a highly
noisy and uncertain setting. To infer useful information from
failed demonstrations, we use a MultiDonut Algorithm [7] that
minimises the probability of repeating a failed demonstration
and generates new attempts similar but not quite the same as
the demonstration. We compare human demonstrations against
a random strategy and show that human demonstrations
provide useful information and hence yield faster learning,
especially in higher dimensions. We show that learning from
observing failed attempts allows the robot to perform the task
more reliably than any individual demonstrator did. We also
show how this algorithm adapts to gradual deterioration in the
system and increases the chances of success when interacting
with an unreliable system.},
}


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