Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with photorealistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform; Aqua2, an Autonomous Underwater Vehicle (AUV), achieving a state-of-the-art 0.657 mAP@50 for oyster detection.

Infant-robot interaction has been increasingly gaining attention, yet, there are limited studies on the development of robot-assisted environments that promote perceptual-motor development in infants. This paper assesses the feasibility of operating a spherical mobile robot, Sphero, to engage infants in perceptual-motor exploration of an open area. Two case scenarios were considered. In the first case, Sphero was the only robot providing stimuli in the environment. In the second case, two additional robots provided stimuli along with Sphero. Pilot data from two infants were analyzed to extract information on their visual attention to and physical interaction with Sphero, as well as their motor actions. Overall, infants (i) expressed a preference to Sphero regardless of stimulation levels, and (ii) moved out of stationary postures in an effort to chase and approach Sphero. These preliminary findings provide support for the future implementation of Sphero in robot-assisted learning environments to promote perceptual-motor development in infants.

Marine autonomous vehicles deployed to conduct marine geophysical surveys are becoming an increasingly used asset in the commercial, academic, and defense industries. However, the ability to collect high-quality data from applicable sensors is directly related to the robustness of vehicle motion caused by environmental disturbances. In this paper we designed and integrated a new path following controller on an autonomous surface vehicle (ASV) that minimizes the linear and angular accelerations on the sensor's local frame. Simulation and experimental results verify reduction of vehicle motion, improvement in path following, and improvement in preliminary sonar data quality compared to that of the existing proportional-yaw path following controller.

Accidental or deliberate disruption of the coordination function in a multi-agent system has been discussed and referred to in the social sciences literature as leader decapitation; this paper outlines a methodology for making multi-agent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system's task specification and simultaneously optimize their strategy to serve the common goal.