Autonomous robotic systems are complex, hybrid, and often safety critical; this makes their
formal specification and verification uniquely challenging. Though commonly used, testing …

Integrating task and motion planning is becoming increasingly important due to the
recognition that a growing number of robotics applications in navigation, search-and-rescue …

We present a reinforcement learning (RL) frame-work to synthesize a control policy from a
given linear temporal logic (LTL) specification in an unknown stochastic environment that …

We propose a cooperative motion and task planning scheme for multi-agent systems where
the agents have independently assigned local tasks, specified as linear temporal logic …

This paper describes a framework for automatically generating optimal action-level behavior
for a team of robots based on temporal logic mission specifications under resource …

In this paper we present a method for automatic planning of optimal paths for a group of
robots that satisfy a common high-level mission specification. The motion of each robot is …

Many existing approaches for coordinating heterogeneous teams of robots either consider
small numbers of agents, are application-specific, or do not adequately address common …

This article proposes a new highly scalable and asymptotically optimal control synthesis
algorithm from linear temporal logic specifications, called STyLu S* for large-Scale optimal …

We study the problem of plan synthesis for multi-agent systems, to achieve complex, high-
level, long-term goals that are assigned to each agent individually. As the agents might not …

Effective deployment of multi-robot teams requires solving several interdependent problems
at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must …