Gonzalo Tejera

Agricultural production is a cornerstone of Uruguay's economy, contributing significantly to its GDP with an impact of approximately 11 points. This sector is characterized by its high technological demand and a declining labor force. Traditionally, the agricultural workforce has not required extensive technical skills, but the rapid advancement of technology necessitates a shift towards more technologically adept labor. Thus, the adoption of advanced technologies and the retraining of the workforce are imperative. Labor retraining within the agricultural domain is essential to fortify the sector's competitiveness, sustainability, and resilience amidst contemporary challenges. Precision agriculture advocates integrating cutting-edge technologies to optimize crop management and agricultural resource utilization. Implementing autonomous robotics has the potential to mitigate labor requirements, foster workforce technological education, and propel advancements toward precision agriculture. Founded in 2013, the MINA group at the Faculty of Engineering of the University of the Republic (Uruguay) has been actively engaged in robotics projects tailored for agricultural applications. Initially focusing on orchards of pome fruits from 2013 to 2023, the group undertook tasks such as harvest support and estimation of harvest quantity and quality. Subsequently, efforts have been directed towards pest control measures targeting pests such as birds and ants, and weed management. This paper delineates the intricacies of these distinct projects, elucidating the technologies employed and developed, outlining achieved results to date, and envisaging the potential for widespread adoption of this technology at a feasible cost.

In light of escalating concerns over climate change, harnessing oceanic data becomes increasingly urgent. Oceans serve as linchpins in understanding the intricate dynamics governing climate phenomena, exerting pivotal influence over global weather patterns and ecological systems. Despite scientific consensus on climate change impacts, including temperature shifts and acidification, a lack of data infrastructure hampers understanding marine ecosystems.This paper presents an adaptive control system for autonomous sailboats that aims to navigate efficiently in varied conditions by favoring the acquisition of oceanographic data. Combining reinforcement and imitation learning, the controller emulates human decision-making, enabling robust navigation. While showcasing promising results, challenges persist in adverse varied conditions. This challenge is exacerbated when confronting necessitating adaptive control mechanisms resilient to wear and tear.Simulators are vital for training due to the need for vast data volumes. Real-world data collection is costly and risky, while simulations accelerate learning. This study employs a simulator operating at ten times real-time speed, significantly simplifying scenario generation by adjusting factors such as sailboat position, orientation, target location, water current, and wind.Nevertheless, this novel approach signifies progress in addressing climate challenges and advancing oceanic research, using advanced computational methods

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.

Robotito is an omnidirectional robot designed and developed at Universidad de la República, Uruguay. It is part of a research line in educational robotics aimed at developing free software and open hardware robots for educational use. It was developed in 2018 and has been used in various research projects and educational activities since then. The demo aims to present the robot and its two programming interfaces (tangible and digital interface) to the IDC community to discuss its use in research and education, identify possible extensions and improvements, and encourage international collaborations.