  Active learning from demonstration allows a robot to query a human for
specific types of input to achieve efficient learning. Existing work has
explored a variety of active query strategies; however, to our knowledge, none
of these strategies directly minimize the performance risk of the policy the
robot is learning. Utilizing recent advances in performance bounds for inverse
reinforcement learning, we propose a risk-aware active inverse reinforcement
learning algorithm that focuses active queries on areas of the state space with
the potential for large generalization error. We show that risk-aware active
learning outperforms standard active IRL approaches on gridworld, simulated
driving, and table setting tasks, while also providing a performance-based
stopping criterion that allows a robot to know when it has received enough
demonstrations to safely perform a task.
