Student opportunities

Evolutionary Robotics

  • Voxel-based soft robots
    • Design and development of a neuroevolution approach for building voxel-based soft robot controllable modular components
    • Learning techniques for the controllers of voxel-based soft robots: reinforcement learning
    • Auto-assembly of voxel-based soft-robots
    • Resolution-agnostic representation for evolution of closed-loop controllers
  • Real robots
    • Design and development of an sw framework for experimenting with learning techniques
    • Hybrid (real vs. simulated) controller learning for addressing the reality gap
    • Reality-gap in multi-agent systems with communication
    • PushGP on mobile robots (Thymio-II): a machine-friendly representation for robotic agents controllers

Artificial Life

  • Multi-agent systems
    • Evolution of behavioral rules for multi-agent systems: the case of self-driving cars
  • Development of an Artificial Life simulator for investigating the Alife-Human interactions
  • Development of an Artificial Life simulator for investigating the evo-devo-niching interplay
  • Design and development of an Artificial Life system for building living logos for the web

See also the student opportunities at the Machine Learning Lab.