<p>As the demand for renewable energy sources continues to increase, solar energy is becoming an increasingly popular option. Therefore, effective training in solar energy system design and operation is crucial to ensure the successful implementation of solar energy technology. To make this training accessible to a wide range of people from different backgrounds, it is important to develop effective and engaging training methods. Immersive virtual reality (VR) has emerged as a promising tool for enhancing solar energy training and education. In this paper, we present a unique approach to evaluating the effectiveness of an immersive VR experience for solar energy systems design training, using a multi-module approach and a detailed analysis of user engagement. To better understand the effectiveness of this VR experience, we divided our experiment into several scenes and employed a range of sensors, including eye-tracking and wireless wearable sensors, to accurately assess users' engagement and performance in each scene. Our results demonstrate that the immersive VR experience was effective in improving users' understanding of solar energy systems design and their ability to perform complex tasks. Moreover, by using sensors to measure user engagement, we identified specific areas that required improvement and provide insights for enhancing the design of future VR training experiences for solar energy systems design. Our study highlights the potential of immersive VR as a tool for enhancing solar energy training and education, with implications for both research and practice.</p>

<p>As the demand for renewable energy sources continues to increase, solar energy is becoming an increasingly popular option. Therefore, effective training in solar energy system design and operation is crucial to ensure the successful implementation of solar energy technology. To make this training accessible to a wide range of people from different backgrounds, it is important to develop effective and engaging training methods. Immersive virtual reality (VR) has emerged as a promising tool for enhancing solar energy training and education. In this paper, we present a unique approach to evaluating the effectiveness of an immersive VR experience for solar energy systems design training, using a multi-module approach and a detailed analysis of user engagement. To better understand the effectiveness of this VR experience, we divided our experiment into several scenes and employed a range of sensors, including eye-tracking and wireless wearable sensors, to accurately assess users' engagement and performance in each scene. Our results demonstrate that the immersive VR experience was effective in improving users' understanding of solar energy systems design and their ability to perform complex tasks. Moreover, by using sensors to measure user engagement, we identified specific areas that required improvement and provide insights for enhancing the design of future VR training experiences for solar energy systems design. Our study highlights the potential of immersive VR as a tool for enhancing solar energy training and education, with implications for both research and practice.</p>

While humans are aware of their body and capabilities, robots are not. To address this, we propose a first step towards a basic, minimal self-awareness in a robot. That is, we propose an experimental methodology to evaluate whether the robot can differentiate itself from the environment, and to test whether artificial self-awareness increases a robot's selfcertainty in an unseen environment. For this, we implemented a simple neural network architecture that enables a dual-arm robot to differentiate its limbs from an environment using visual and proprioception sensory inputs. The proposed experimental approach allows us to evaluate whether the robot can differentiate itself from the environment. Our results indicate that a robot can distinguish itself with an accuracy of 88.7% on average in different environmental settings and under confounding input signals.

While humans are aware of their body and capabilities, robots are not. To address this, we present in this paper a neural network architecture that enables a dualarm robot to get a sense of itself in an environment. Our approach is inspired by human self-awareness developmental levels and serves as the underlying building block for a robot to achieve awareness of itself while carrying out tasks in an environment. We assume that a robot has to know itself before interacting with the environment in order to be able to support different robotic tasks. Hence, we implemented a neural network architecture to enable a robot to differentiate its limbs from the environment using visual and proprioception sensory inputs. We demonstrate experimentally that a robot can distinguish itself with an accuracy of 88.7% on average in cluttered environmental settings and under confounding input signals.