An analysis of the role of different levels of exchange of explicit information in human-robot cooperation.

For smooth human-robot cooperation, it is crucial that robots understand social cues from humans and respond accordingly. Contextual information provides the human partner with real-time insights into how the robot interprets social cues and what action decisions it makes as a result. We propose and implement a novel design for a human-robot cooperation framework that uses augmented reality and user gaze to enable bidirectional communication. Through this framework, the robot can recognize the objects in the scene that the human is looking at and infer the human's intentions within the context of the cooperative task. We proposed three levels of exchange of explicit information designs, each providing increasingly more information. These designs enable the robot to offer contextual information about what user actions it has identified and how it intends to respond, which is in line with the goal of cooperation. We report a user study (n = 24) in which we analyzed the performance and user experience with the three different levels of exchange of explicit information. Results indicate that users preferred an intermediate level of exchange of information, in which users knew how the robot was interpreting their intentions, but where the robot was autonomous to take unsupervised action in response to gaze input from the user, needing a less informative action from the human's side.