  Traditional imitation learning provides a set of methods and algorithms to
learn a reward function or policy from expert demonstrations. Learning from
demonstration has been shown to be advantageous for navigation tasks as it
allows for machine learning non-experts to quickly provide information needed
to learn complex traversal behaviors. However, a minimal set of demonstrations
is unlikely to capture all relevant information needed to achieve the desired
behavior in every possible future operational environment. Due to
distributional shift among environments, a robot may encounter features that
were rarely or never observed during training for which the appropriate reward
value is uncertain, leading to undesired outcomes. This paper proposes a
Bayesian technique which quantifies uncertainty over the weights of a linear
reward function given a dataset of minimal human demonstrations to operate
safely in dynamic environments. This uncertainty is quantified and incorporated
into a risk averse set of weights used to generate cost maps for planning.
Experiments in a 3-D environment with a simulated robot show that our proposed
algorithm enables a robot to avoid dangerous terrain completely in two out of
three test scenarios and accumulates a lower amount of risk than related
approaches in all scenarios without requiring any additional demonstrations.
