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.

IDIL 是一种新颖的模仿学习算法，旨在模拟专家的多样化意图驱动行为。其通过从不同示范中估计专家意图并将其用于学习意图感知模型，能够胜任高维状态表示下的顺序任务，而不涉及相关技术的复杂性和缺点。创造性地生成模型的实证结果表明，IDIL 所生成的模型在任务性能指标上要么与最新的模仿学习基准相匹敌，要么超越其表现。此外，作为一种生成模型，IDIL 在人机交互关键的意图推断指标上表现出卓越的性能，并恰如其分地捕捉了广泛的专家行为。