  We explore beyond existing work on learning from demonstration by asking the
question: Can robots learn to teach?, that is, can a robot autonomously learn
an instructional policy from expert demonstration and use it to instruct or
collaborate with humans in executing complex tasks in uncertain environments?
In this paper we pursue a solution to this problem by leveraging the idea that
humans often implicitly decompose a higher level task into several subgoals
whose execution brings the task closer to completion. We propose Dirichlet
process based non-parametric Inverse Reinforcement Learning (DPMIRL) approach
for reward based unsupervised clustering of task space into subgoals. This
approach is shown to capture the latent subgoals that a human teacher would
have utilized to train a novice. The notion of action primitive is introduced
as the means to communicate instruction policy to humans in the least
complicated manner, and as a computationally efficient tool to segment
demonstration data. We evaluate our approach through experiments on hydraulic
actuated scaled model of an excavator and evaluate and compare different
teaching strategies utilized by the robot.
