Reinforcement learning algorithms discover policies that maximize reward, but do not
necessarily guarantee safety during learning or execution phases. We introduce a new …

This paper targets the efficient construction of a safety shield for decision making in
scenarios that incorporate uncertainty. Markov decision processes (MDPs) are prominent …

Abstract Model checking is a computer-assisted method for the analysis of dynamical
systems that can be modeled by state-transition systems. Drawing from research traditions in …

In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

A machine-learned system that is fair in static decision-making tasks may have biased
societal impacts in the long-run. This may happen when the system interacts with humans …

In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach
to solving real-world tasks. However, despite their successes, DRL-based policies suffer …

Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to
teach robots new tasks, enabling them to customize the robot behavior. However, modern …

Abstract Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm
that can generate approximate policies for large Partially Observable Markov Decision …