In this paper, we propose an optimization-based decision-making tool for safe motion
planning and control in an environment with randomly moving obstacles. The unique feature …

We consider the stochastic shortest path planning problem in MDPs, ie, the problem of
designing policies that ensure reaching a goal state from a given initial state with minimum …

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …

We present a general framework for risk semantics on Signal Temporal Logic (STL)
specifications for stochastic dynamical systems using axiomatic risk theory. We show that …

Keeping risk under control is a primary objective in many critical real-world domains,
including finance and healthcare. The literature on risk-averse reinforcement learning (RL) …

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 …

Risk occupies a central role in both the theory and practice of decision-making. Although it is
deeply implicated in many conditions involving dysfunctional behavior and thought, modern …

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of
parameters or parameterized input signals. Given a system specification in Signal Temporal …

We study the consideration of fairness in redundant assignment for multi-agent task
allocation. It has recently been shown that redundant assignment of agents to tasks provides …

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole
depends upon careful planning in space and time due to highly dynamic solar illumination …