Discrete event and hybrid systems provide methodologies for modeling of robotic tasks prone to analysis and synthesis based on (qualitative and quantitative) performance evaluation. Examples of applications to model abstractions, multi-robot task planning, robot population distribution control, and stochastic plan optimization by reinforcement learning will illustrate work direction currently pursued at the Intelligent Systems Lab of ISR/IST on this topic.