In this article, a risk-aware motion control scheme is considered for mobile robots to avoid
randomly moving obstacles when the true probability distribution of uncertainty is unknown …

In this paper, we propose a Wasserstein metric-based random path planning algorithm.
Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the …

Modern navigation algorithms based on deep reinforcement learning (RL) show promising
efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral …

With the recent advent of risk awareness, decision-making algorithms' complexity increases,
posing a severe difficulty to solve such formulations of the problem online. Our approach is …

We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic
systems. We present Risk-Averse Nonlinear Steering RRT*(RANS-RRT*) as an RRT …

This paper considers a risk-constrained infinite-horizon optimal control problem and
proposes to solve it in an iterative manner. Each iteration of the algorithm generates a …

This article builds Wasserstein ambiguity sets for the unknown probability distribution of
dynamic random variables leveraging noisy partial-state observations. The constructed …

The effective deployment of robots in risky and crowded environments (RCE) requires the
specification of robot plans that are consistent with humans' behaviors. As is well known …

Successful robotic operation stochastic environments relies on accurate characterization of
the underlying probability distributions, yet this is often imperfect due to limited knowledge …

This article introduces a disturbance-parametrized (DP) robust lattice-based motion-
planning framework for nonlinear systems affected by bounded disturbances. A key idea in …