  When faced with accomplishing a task, human experts exhibit intentional
behavior. Their unique intents shape their plans and decisions, resulting in
experts demonstrating diverse behaviors to accomplish the same task. Due to the
uncertainties encountered in the real world and their bounded rationality,
experts sometimes adjust their intents, which in turn influences their
behaviors during task execution. This paper introduces IDIL, a novel imitation
learning algorithm to mimic these diverse intent-driven behaviors of experts.
Iteratively, our approach estimates expert intent from heterogeneous
demonstrations and then uses it to learn an intent-aware model of their
behavior. Unlike contemporary approaches, IDIL is capable of addressing
sequential tasks with high-dimensional state representations, while
sidestepping the complexities and drawbacks associated with adversarial
training (a mainstay of related techniques). Our empirical results suggest that
the models generated by IDIL either match or surpass those produced by recent
imitation learning benchmarks in metrics of task performance. Moreover, as it
creates a generative model, IDIL demonstrates superior performance in intent
inference metrics, crucial for human-agent interactions, and aptly captures a
broad spectrum of expert behaviors.
