  While imitation learning is often used in robotics, the approach frequently
suffers from data mismatch and compounding errors. DAgger is an iterative
algorithm that addresses these issues by aggregating training data from both
the expert and novice policies, but does not consider the impact of safety. We
present a probabilistic extension to DAgger, which attempts to quantify the
confidence of the novice policy as a proxy for safety. Our method,
EnsembleDAgger, approximates a Gaussian Process using an ensemble of neural
networks. Using the variance as a measure of confidence, we compute a decision
rule that captures how much we doubt the novice, thus determining when it is
safe to allow the novice to act. With this approach, we aim to maximize the
novice's share of actions, while constraining the probability of failure. We
demonstrate improved safety and learning performance compared to other DAgger
variants and classic imitation learning on an inverted pendulum and in the
MuJoCo HalfCheetah environment.
