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Robot learning from demonstration (RLfD) seeks to enable lay users to encode desired robot behaviors as autonomous controllers. Current work uses a humanís demonstration of the target task to initialize the robotís policy, and then improves its performance either through practice (with a known reward function), or additional human interaction. In this article, we focus on the initialization step and consider what can be learned when the humans do not provide successful examples. We develop probabilistic approaches that avoid reproducing observed failures while leveraging the variance across multiple attempts to drive exploration. Our experiments indicate that failure data do contain information that can be used to discover successful means to accomplish tasks. However, in higher dimensions, additional information from the user will most likely be necessary to enable efficient failure-based learning.


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Last update: 25/08/06