  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.
