Human-Robot Interaction Through Mixed-Initiative Planning for Rescue and Search Rovers

2005 ◽  
pp. 483-494 ◽  
Author(s):  
Alberto Finzi ◽  
Andrea Orlandini
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Mixed Initiative

scholarly journals Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 296 ◽  
Author(s):  
Caroline P. C. Chanel ◽  
Raphaëlle N. Roy ◽  
Frédéric Dehais ◽  
Nicolas Drougard
Keyword(s):  
Human Performance ◽  
Cardiac Activity ◽  
Human Robot Interaction ◽  
Operational Performance ◽  
Artificial Agents ◽  
Robot Interaction ◽  
Input Devices ◽  
Level Of Automation ◽  
Mixed Initiative ◽  
And Behavior

The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.

scholarly journals How Can Physiological Computing Benefit Human-Robot Interaction?

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 100
Author(s):  
Raphaëlle N. Roy ◽  
Nicolas Drougard ◽  
Thibault Gateau ◽  
Frédéric Dehais ◽  
Caroline P. C. Chanel
Keyword(s):  
Mental States ◽  
Task Demands ◽  
Human Robot Interaction ◽  
Cognitive Resources ◽  
Time On Task ◽  
Learning Tools ◽  
Robot Interaction ◽  
Planning Technique ◽  
And Performance ◽  
Mixed Initiative

As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction.

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