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 …

We study a safe reinforcement learning problem in which the constraints are defined as the
expected cost over finite-length trajectories. We propose a constrained cross-entropy-based …

We present a method for learning multi-stage tasks from demonstrations by learning the
logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula …

In this paper, we investigate the problem of planning an optimal infinite path for a single
robot to achieve a linear temporal logic (LTL) task with security guarantee. We assume that …

We present a method for learning multi-stage tasks from demonstrations by learning the
logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula …

Given a description of system dynamics, input constraints, and a cost function, the problem
of optimal control is to find a sequence of inputs and a state trajectory that minimizes the total …

Trustworthy robots must be able to complete tasks reliably while obeying safety constraints.
While traditional methods for constrained motion planning and optimal control can achieve …

Reinforcement learning (RL) has been widely used, for example, in robotics,
recommendation systems, and financial services. Existing RL algorithms typically optimize …

While autonomous robot systems are becoming increasingly common, their usage is still
mostly limited to rather simple tasks. This primarily results from the need for manually …