  Learning from demonstration (LfD) techniques seek to enable novice users to
teach robots novel tasks in the real world. However, prior work has shown that
robot-centric LfD approaches, such as Dataset Aggregation (DAgger), do not
perform well with human teachers. DAgger requires a human demonstrator to
provide corrective feedback to the learner either in real-time, which can
result in degraded performance due to suboptimal human labels, or in a post hoc
manner which is time intensive and often not feasible. To address this problem,
we present Mutual Information-driven Meta-learning from Demonstration (MIND
MELD), which meta-learns a mapping from poor quality human labels to predicted
ground truth labels, thereby improving upon the performance of prior LfD
approaches for DAgger-based training. The key to our approach for improving
upon suboptimal feedback is mutual information maximization via variational
inference. Our approach learns a meaningful, personalized embedding via
variational inference which informs the mapping from human provided labels to
predicted ground truth labels. We demonstrate our framework in a synthetic
domain and in a human-subjects experiment, illustrating that our approach
improves upon the corrective labels provided by a human demonstrator by 63%.
