Abstract
Social robots have an enormous potential for educational applications and allow for cognitive outcomes that are similar to those with human involvement. Remotely controlling a social robot to interact with students and peers in an immersive fashion opens up new possibilities for instructors and learners alike. Using immersive approaches can promote engagement and have beneficial effects on remote lesson delivery and participation. However, the performance and power consumption associated with the involved devices are often not sufficiently contemplated, despite being particularly important in light of sustainability considerations. The contributions of this research are thus twofold. On the one hand, we present telepresence solutions for a social robot’s location-independent operation using (a) a virtual reality headset with controllers and (b) a mobile augmented reality application. On the other hand, we perform a thorough analysis of their power consumption and system performance, discussing the impact of employing the various technologies. Using the QTrobot as a platform, direct and immersive control via different interaction modes, including motion, emotion, and voice output, is possible. By not focusing on individual subsystems or motor chains, but the cumulative energy consumption of an unaltered robot performing remote tasks, this research provides orientation regarding the actual cost of deploying immersive robotic telepresence solutions.
Highlights
The potential of social robots for educational applications is enormous, allowing cognitive outcomes that are similar to those with human involvement [1]
While many research efforts focus on aspects related to autonomous and cognitive robotics for education [2,3,4,5], enabling learners and instructors to control a social robot remotely and to immersively interact with their peers and students opens up further possibilities for effective lesson delivery, participation, and tutoring in the classroom
The experiment results are presented below as follows: Firstly, we show the overall real power consumption obtained from the power meter device; secondly, we focus on the software results, where the central processing unit (CPU) and random access memory (RAM) consumption is evaluated
Summary
The potential of social robots for educational applications is enormous, allowing cognitive outcomes that are similar to those with human involvement [1]. While many research efforts focus on aspects related to autonomous and cognitive robotics for education [2,3,4,5], enabling learners and instructors to control a social robot remotely and to immersively interact with their peers and students opens up further possibilities for effective lesson delivery, participation, and tutoring in the classroom. Educational research distinguishes various communication mechanisms between students and instructors, i.e., teachers or tutors, which include non-verbal clues that are visible to the instructor during the lesson [7,8]. These clues involve monitoring and tracking motion to different extents and the time that students spend looking at materials or looking away. A social robot with diverse interaction modalities would increase the quality and amount of feedback delivered to students
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