Robot control for tasks such as moving around obstacles or grasping objects has advanced
significantly in the last few decades. However, controlling robots to perform complex tasks is …

In control theory, complicated dynamics such as systems of (nonlinear) differential equations
are controlled mostly to achieve stability. This fundamental property, which can be with …

The need for computationally-efficient control methods of dynamical systems under temporal
logic tasks has recently become more apparent. Existing methods are computationally …

We show that artificial neural networks with rectifier units as activation functions can exactly
represent the piecewise affine function that results from the formulation of model predictive …

We tackle the challenging problem of multi-agent cooperative motion planning for complex
tasks described using signal temporal logic (STL), where robots can have nonlinear and …

We present a counterexample-guided inductive synthesis approach to controller synthesis
for cyber-physical systems subject to signal temporal logic (STL) specifications, operating in …

This paper addresses the problem of learning optimal policies for satisfying signal temporal
logic (STL) specifications by agents with unknown stochastic dynamics. The system is …

Control synthesis from temporal logic specifications has gained popularity in recent years. In
this paper, we use a model predictive approach to control discrete time linear systems with …

Signal Temporal Logic (STL) provides a convenient way of encoding complex control
objectives for robotic and cyber-physical systems. The state-of-the-art in trajectory synthesis …

This paper presents a technique, named STLCG, to compute the quantitative semantics of
Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a …