Assistive robots working on domestic environments need to be able to interact with garments and other deformable objects to perform some tasks. This line of research is devoted to developing methods for garment perception and manipulation applied to domestic laundry tasks such as ironing, folding or hanging garments, using Deep Learning techniques.
There are new exciting applications of robots in entertainment, such as using them as physical avatar in a great variety of games. But using physical robots poses some limitations in terms of safety, robot abilities, etc. This line of research studies how to alleviate or remove some of these constraints using augmented/mixed reality to add virtual elements to the games.
As opposed to typical robots, that are designed specifically to solve concrete tasks, modular robots combine several generic modules to form robots that can adapt themselves to different situations and tasks. This line of research studies methods to coordinate the self-organization of these modules to solve the locomotion problem under different morphologies.
More than 170 hours of university teaching experience + 180 hours of non-university teaching experience, including the following courses:
Addicionally, I have directed the following Bachelor Theses:
