  In the learning from demonstration (LfD) paradigm, understanding and
evaluating the demonstrated behaviors plays a critical role in extracting
control policies for robots. Without this knowledge, a robot may infer
incorrect reward functions that lead to undesirable or unsafe control policies.
Recent work has proposed an LfD framework where a user provides a set of formal
task specifications to guide LfD, to address the challenge of reward shaping.
However, in this framework, specifications are manually ordered in a
performance graph (a partial order that specifies relative importance between
the specifications). The main contribution of this paper is an algorithm to
learn the performance graph directly from the user-provided demonstrations, and
show that the reward functions generated using the learned performance graph
generate similar policies to those from manually specified performance graphs.
We perform a user study that shows that priorities specified by users on
behaviors in a simulated highway driving domain match the automatically
inferred performance graph. This establishes that we can accurately evaluate
user demonstrations with respect to task specifications without expert
criteria.
