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 …

Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …

Traditionally, autonomous cars make predictions about other drivers' future trajectories, and
plan to stay out of their way. This tends to result in defensive and opaque behaviors. Our key …

In control theory, complex models of physical processes, such as systems of differential or
difference equations, are usually checked against simple specifications, such as stability …

Abstract The term Cyber-Physical Systems (CPS) typically refers to engineered, physical
and biological systems monitored and/or controlled by an embedded computational core …

This paper describes current work on framing the model predictive control (MPC) of cyber-
physical systems as synthesis from signal temporal logic (STL) specifications. We provide a …

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 …

Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They
predict their future trajectories and plan to stay out of their way. While physically safe, this …

A cyber-physical system (CPS) is an integration of computation with physical processes
whose behavior is defined by both computational and physical parts of the system. In this …

In order to collaborate safely and efficiently, robots need to anticipate how their human
partners will behave. Some of today's robots model humans as if they were also robots, and …