Letícia Berto (she/her) is a postdoctoral researcher at the Cognitive Robotics Lab at the University of Manchester. Her research focuses on developing cognitive abilities in robots, taking inspiration from child development. She is particularly interested in building cognitive autonomous robots through the design of cognitive architectures, with an emphasis on intrinsic motivation, learning, and decision-making processes. In her work, she tackles complex challenges, including environmental resource availability, social interaction dynamics, the distinction between wanting and liking, and the application of the theory of mind to enhance social interactions among autonomous cognitive agents.

One of his earliest memories is of doodling weird robots on any piece of paper (or wall) he could find. Marco Gabriele Fedozzi (he/him) still brings that original passion into his current Ph.D. research, funded by the University of Genoa, at the COgNitive Architectures for Collaborative Technologies (CONTACT) Unit of the Italian Institute of Technology. Over the years, his focus has shifted—from designing (and dreaming of piloting) giant robots to exploring how smaller robots can help us peer into the human mind. While his ultimate goal is to enable robots and humans to understand and interact with each other naturally, he soon realized that human behavior can’t be separated from the way we learn. This insight sparked his current fascination with the emergence of intentionality in infants—a phenomenon he is now working to model and replicate in synthetic embodied agents. He may or may not still be plotting how to transfer that knowledge to giant robots someday.

Renan Lima Baima (he/him) is a Doctoral Researcher at the University of Luxembourg within the FINATRAX/FiReSpARX group. He is particularly interested in how autonomous embodied agents can learn to negotiate, collaborate, and adapt in complex environments—whether on Earth or in space. His research integrates developmental learning principles and intrinsic motivation into both single-embodied and multi-robot systems, aiming to build scalable, adaptive architectures that behave autonomously in real-world scenarios. Renan’s work ranges from designing decentralized frameworks for multi-robot economic coordination to teaching humanoid robots object affordances through hierarchical, curiosity-driven learning architectures. His goal is to move beyond human-centric robotic models, developing agents as self-driven learners and decision-makers in their own way.