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Robot Learning with Crash Constraints
[article]
2021
arXiv
pre-print
In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter failing behaviors as the learning loop progresses. Specifically, in robot applications where failing is undesired but not catastrophic, many algorithms struggle with leveraging data obtained from failures. This is usually caused by (i) the failed experiment ending prematurely, or (ii) the acquired data being scarce or
arXiv:2010.08669v3
fatcat:4cgvybs3dvbbbdv4qmqzprobn4