Operator Engagement Detection for Robot Behavior Adaptation

It has been shown that in human-robot interaction, the effectiveness of a robot varies inversely with the operator engagement in the task. Given the importance of maintaining optimal task engagement when working with a robot, it would be immensely useful to have a robotic system that can detect the level of operator engagement and modify its behavior if required. This paper presents a framework for human-robot interaction that allows inference of operator's engagement level through the analysis of his/her physiological signals, and adaptation of robot behavior as a function of the operator's engagement level. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The results from affect-elicitation tasks for human participants showed that it was possible to detect engagement through physiological sensing in real-time. An open-loop teleoperation-based robotic experiment was also conducted where the recorded physiological signals were transmitted to the robot in real-time speed to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator engagement level

12 A New Approach to Implicit Human-Robot Interaction Using Affective Cues

Abstract – It is well known that in social interactions, implicit communication between the communicators plays a significant role. It would be immensely useful to have a robotic system that is capable of such implicit communication with the operator and can modify its behavior if required. This paper presents a framework for human-robot interaction in which the operator's physiological signals were analyzed to infer his/her probable anxiety level and robot behavior was adapted as a function of the operator affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The target affective state chosen in this work was anxiety. The results from affect-elicitation tasks for human participants showed that it is possible to detect anxiety through physiological sensing in real-time. A robotic experiment was also conducted to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator anxiety level

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human–robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper, we present a comparative study of four machine learning methods—K-Nearest Neighbor, Regression Tree (RT), Bayesian Network and Support Vector Machine (SVM) as applied to the domain of affect recognition using physiological signals. The results showed that SVM gave the best classification accuracy even though all the methods performed competitively. RT gave the next best classification accuracy and was the most space and time efficient

Dynamic Difficulty Adjustment Impact on Players' Confidence

International audienceDifficulty is one of the major motivational pull of video games, and thus many games use Dynamic Difficulty Adjustment (DDA) systems to improve the game experience. This paper describes our research investigating the influence of DDA systems on player's confidence, evaluated using an in-game bet system. Our hypothesis is that DDA systems may lead players to overconfidence, revealed by an overestimation of their success chances when betting. This boost of confidence may be a part of the positive impact of DDA systems on the quality of game experience. We explain our method to evaluate player's confidence and implement it into three games related to logical, motor and sensory difficulties. We describe two experimental conditions where difficulty is either randomly chosen or adapted using a DDA algorithm. Results show how DDA systems can lead players to high level of overconfidence