We investigate robust model-free reinforcement learning algorithms designed for
environments that may be dynamic or even adversarial. Traditional state-based policies …

We approach the fundamental problem of obstacle avoidance for robotic systems via the
lens of online learning. In contrast to prior work that either assumes worst-case realizations …

We study the problem of multi-agent control of a dynamical system with known dynamics
and adversarial disturbances. Our study focuses on optimal control without centralized …

A compact, reliable, and straightforward mathematical modeling of the eddy current (EC)
braking torque is proposed in this article. First, the braking magnetic force, which curbs the …

Modern robots require accurate forecasts to make optimal decisions in the real world. For
example, self-driving cars need an accurate forecast of other agents' future actions to plan …

We propose a method to attack controllers that rely on external timeseries forecasts as task
parameters. An adversary can manipulate the costs, states, and actions of the controllers by …

Abstract Machine learning tools are widely used for knowledge extraction, modeling, and
decision tasks; a range of problems that Control Theory also tackles. Their relations have …

The control of dynamical systems is a fundamental problem with a vast array of applications,
from robotics to biological engineering. Recently, the game-theoretic primitive of regret …

Modern robotic applications have been propelled by exciting advancements in rich sensor
technologies such as LiDAR and RGB-D cameras. However, when we deploy our robots in …

A simple and a straightforward mathematical modeling of the eddy current generated on a
retarded rotating disk (RD) is proposed in this paper. First, the braking magnetic force, which …