We present an historical overview about the connections between the analysis of risk and
the control of autonomous systems. We offer two main contributions. Our first contribution is …

Although ground robotic autonomy has gained widespread usage in structured and
controlled environments, autonomy in unknown and off-road terrain remains a difficult …

Abstract Purpose of Review Deploying a team of robots that can carefully coordinate their
actions can make the entire system robust to individual failures. In this report, we review …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

We propose a risk-aware motion planning and decision-making method that systematically
adjusts the safety and conservativeness in an environment with randomly moving obstacles …

Current value-based multi-agent reinforcement learning methods optimize individual Q
values to guide individuals' behaviours via centralized training with decentralized execution …

Planning safe trajectories for nonlinear dynamical systems subject to model uncertainty and
disturbances is challenging. In this work, we present a novel approach to tackle chance …

Ensuring the safety of autonomous vehicles (AV s) in uncertain traffic scenarios is a major
challenge. In this paper, we address the problem of computing the risk that AV s violate a …

The distributional perspective on reinforcement learning (RL) has given rise to a series of
successful Q-learning algorithms, resulting in state-of-the-art performance in arcade game …

Enforcing safety in the presence of stochastic uncertainty is a challenging problem.
Traditionally, researchers have proposed safety in the statistical mean as a safety measure …