The specification of complex motion goals through temporal logics is increasingly favored in
robotics to narrow the gap between task and motion planning. A major limiting factor of such …

Motivated by robotic motion planning, we develop a framework for control policy synthesis
for both non-deterministic transition systems and Markov decision processes that are subject …

This paper reviews recent developments extending sampling based motion planning
algorithms to operate in dynamic environments. Sampling based planners provide an …

In this paper we present a method for automatically generating optimal robot trajectories
satisfying high level mission specifications. The motion of the robot in the environment is …

Enabling robots to accomplish sophisticated tasks requires enhancing their capability to
plan at multiple levels of discrete and continuous abstractions. Toward this goal, the …

In this paper we present a method for automatically planning robust optimal paths for a
group of robots that satisfy a common high level mission specification. Each robot's motion in …

Our goal in this paper is to plan the motion of a robot in a partitioned environment with
dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level …

We propose a machine learning (ML)-inspired approach to estimate the relevant region of a
motion planning problem during the exploration phase of sampling-based path-planners …

We develop a method for the control of discrete-time nonlinear systems subject to temporal
logic specifications. Our approach uses a coarse abstraction of the system and an …

To compute collision-free and dynamically-feasibile trajectories that satisfy high-level
specifications given in a planning-domain definition language, this paper proposes to …