Social Impact of Recharging Activity in Long-Term HRI and Verbal Strategies to Manage User Expectations During Recharge

Social robots perform tasks to help humans in their daily activities. However, if they fail to fulfill expectations this may affect their acceptance. This work investigates the service degradation caused by recharging, during which the robot is socially inactive. We describe two studies conducted in an ecologically valid office environment. In the first long-term study (3 weeks), we investigated the service degradation caused by the recharging behavior of a social robot. In the second study, we explored the social strategies used to manage users’ expectations during recharge. Our findings suggest that the use of verbal strategies (transparency, apology, and politeness) can make robots more acceptable to users during recharge

A framework to estimate cognitive load using physiological data

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load

The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces

We present the UK Robotics and Artificial Intelligence Hub for Offshore Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded, multi-site project. The ORCA Hub vision is to use teams of robots and autonomous intelligent systems (AIS) to work on offshore energy platforms to enable cheaper, safer and more efficient working practices. The ORCA Hub will research, integrate, validate and deploy remote AIS solutions that can operate with existing and future offshore energy assets and sensors, interacting safely in autonomous or semi-autonomous modes in complex and cluttered environments, co-operating with remote operators. The goal is that through the use of such robotic systems offshore, the need for personnel will decrease. To enable this to happen, the remote operator will need a high level of situation awareness and key to this is the transparency of what the autonomous systems are doing and why. This increased transparency will facilitate a trusting relationship, which is particularly key in high-stakes, hazardous situations.Comment: 2 pages. Peer reviewed position paper accepted in the Explainable Robotic Systems Workshop, ACM Human-Robot Interaction conference, March 2018, Chicago, IL US

A model of contingency detection to spot tutoring behavior and respond to ostensive cues in human-robot-interaction

Lohan KS. A model of contingency detection to spot tutoring behavior and respond to ostensive cues in human-robot-interaction. Bielefeld: Universitätsbibliothek; 2011