Researchers in biomechanics, neuroscience, human–machine interaction and other fields are interested in inferring human intentions and objectives from observed actions. The problem of inferring objectives from observations has received extensive theoretical and methodological development from both the controls and machine learning communities. In this paper, we provide an integrating view of objective learning from human demonstration data. We differentiate algorithms based on the assumptions made about the objective function structure, how the similarity between the inferred objectives and the observed demonstrations is assessed, the assumptions made about the agent and environment model, and the properties of the observed human demonstrations. We review the application domains and validation approaches of existing works and identify the key open challenges and limitations. The paper concludes with an identification of promising directions for future work.

生物力学、神经科学、人机交互等领域的研究人员对从观察到的行为推断人类的意图和目标感兴趣。从观察中推断目标的问题已经得到了控制和机器学习社区广泛的理论和方法发展。在本文中，我们提供了从人类演示数据中客观学习的集成视图。我们根据对目标函数结构所做的假设、推断目标与观察到的演示之间的相似性如何评估、对代理和环境模型的假设以及观察到的人类演示的属性来区分算法。我们回顾了现有作品的应用领域和验证方法，并确定了关键的开放挑战和局限性。本文最后确定了未来工作的有希望的方向。