In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.

翻译：在涉及人-机器人交互（HRI）、人-机器人协同（HRT）以及人机协作系统的应用中，推断人类伙伴的意图至关重要。本文针对提供完全或部分（例如共享控制）遥操作的移动机器人，提出了一种推断人类操作者导航意图的方法。我们提出了一种机器学习操作者意图推断（MLOII）方法，该方法：a）处理由机器人传感器采集的空间数据；b）利用监督式机器学习算法在线估计操作者最可能的导航目标。通过模拟真实环境下的勘探与远程巡检场景，实验评估了所提方法可靠且高效地推断人类操作者意图的能力。在准确性和不确定性方面的结果表明，该方法与文献中另一种当前最先进的方法性能相当。