Rather than augmenting rewards with penalties for undesired behavior, Constrained
Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing …

Partially observable Markov decision processes (POMDPs) provide a flexible representation
for real-world decision and control problems. However, POMDPs are notoriously difficult to …

Representing and reasoning about uncertainty is crucial for autonomous agents acting in
partially observable environments with noisy sensors. Partially observable Markov decision …

This paper presents a hybrid online Partially Observable Markov Decision Process
(POMDP) planning system that addresses the problem of autonomous navigation in the …

Solving partially observable Markov decision processes (POMDPs) with high dimensional
and continuous observations, such as camera images, is required for many real life robotics …

In this paper, we consider online planning in par-tially observable domains. Solving the
corresponding POMDP problem is a very challenging task, particularly in an online setting …

Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs)
maximize reward objectives while satisfying hard cost constraints, generalizing safe …

Planning under partial obervability is essential for autonomous robots. A principled way to
address such planning problems is the Partially Observable Markov Decision Process …

In teleoperation of redundant robotic manipulators, translating an operator's end effector
motion command to joint space can be a tool for maintaining feasible and precise robot …

Autonomous capabilities could be an essential aspect of future near-Earth asteroid
exploration missions, enabling a fleet of low-cost spacecraft to be distributed to various …