Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction.

We investigate the effect of relational dialogue on creating rapport and exerting social influence in human-robot conversation, by comparing interactions with and without a relational component, and with different agent types. Human participants interact with two agents — a Nao robot and a virtual human — in four dialogue scenarios: one involving building familiarity, and three involving sharing information and persuasion in item-ranking tasks. Results show that both agents influence human decision-making; people prefer interacting with the robot, feel higher rapport with the robot, and believe the robot has more influence; and that objective influence of the agent on the person is increased by building familiarity, but is not significantly different between the agents.

Determining the position of a robot in an environment, termed localization, is one of the challenges facing roboticist. Localization is essential to solving more complex problems such as locomotion, path planning and environmental learning. Our lab is developing a multi-agent system to use multiple small robots to accomplish tasks normally completed by larger robots. However, because of the reduced size of these robots, methods previously used to determine the position of the robot, such as GPS, cannot be employed. The problem we are facing is that we need to be able to determine the position of each of the robots in this multi-agent system simultaneously. We have developed a system to help track and identify robots using an overhead camera and LEDs, mounted on the robots, to efficiently solve the localization problem.