Plan execution in unknown environments poses a number of challenges: uncertainty in
domain modeling, stochasticity at execution time, and the presence of exogenous events …

Given the complexity of real-world, unstructured domains, it is often impossible or impractical
to design models that include every feature needed to handle all possible scenarios that an …

Planning for unknown environments presents a number of technical challenges. The
planner must ensure robustness to unknown phenomena and manage unpredictable …

Despite considerable efforts by human designers, accounting for every unique situation that
an autonomous robotic system deployed in the real world could face is often an infeasible …

Reduced models allow autonomous robots to cope with the complexity of planning in
stochastic environments by simplifying the model and reducing its accuracy. The solution …

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem-a general
framework to model path planning and decision making in stochastic environments with goal …

Existing reduced model techniques simplify a problem by applying a uniform principle to
reduce the number of considered outcomes for all state-action pairs. It is non-trivial to …

The rapid growth in the deployment of autonomous systems across various sectors has
generated considerable interest in how these systems can operate reliably in large …

Plan execution in unknown environments poses a number of challenges: uncertainty in
domain modeling, stochasticity at execution time, and the presence of exogenous events …

Reduced models of large Markov decision processes accelerate planning by considering a
subset of outcomes for each state-action pair. This reduction in reachable states leads to …