The general research line of the lab focuses on how to build bio-inspired autonomous systems capable of learning from their environment and peers in order to solve complex tasks. For this purpose we draw inspirations from nature and investigate bio-inspired techniques such as swarm intelligence (social insect behavior as found in honyebees and ants), reinforcement learning and evolutionary algorithms.
Currently we focus on two tracks.
- We are particularly interested in how to design large-scale distributed, self-organizing systems capable of solving complex coordination tasks, such as foraging for food. These tasks find direct applications in areas as automated patrolling, localization of danger and localization of victims in rescue management scenarios.
- We focus on developing and implementing new learning and filtering algorithms to build individual robots capable of facilitating human tasks, such as for instance adaptive tele-presence robotics.