MONARCA will develop and validate solutions for multi-parametric, long term monitoring of behavioural and physiological information relevant to bipolar disorder.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.
“Scaffolding” is a powerful principle taken from psychology for organizing and optimizing the progress of a learner. The goal in this project is to combine this powerful structuring of a learning process with recent advances in deep and hierarchical reinforcement learning in order to make learning of an artificial agent more efficient and feasible when interaction data is much more limited than considered in current studies.
The human fingernail turns out to be very important when it comes to picking coins, scratching off stickers, or simply probing surfaces. To endow robots with similar capabilities, in this project, we are developing a sensorized fingernail for mechanical hands, i.e. robotic or prostetic hands, capable of measuring vibratory and static contact forces. High sensitivity combined with robustness for daily use are the key features describing our proposed design.
Clothing provides a challenging test domain for research in the field of cognitive robotics. On the one hand, robots have to make use of commonsense knowledge to be able to understand the socially constructed meaning and function of garments. On the other hand, the variance resulting from deformations and differences between individual items of clothing calls for implicit representations which have to be learned from experience. Our robot uses topological, geometric, and subsymbolic knowledge representations for the manipulation of clothes with its anthropomorphic hands. read more »
MONARCA will develop and validate solutions for multi-parametric, long term monitoring of behavioural and physiological information relevant to bipolar disorder.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.
Clothing provides a challenging test domain for research in the field of cognitive robotics. On the one hand, robots have to make use of commonsense knowledge to be able to understand the socially constructed meaning and function of garments. On the other hand, the variance resulting from deformations and differences between individual items of clothing calls for implicit representations which have to be learned from experience. Our robot uses topological, geometric, and subsymbolic knowledge representations for the manipulation of clothes with its anthropomorphic hands.