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 …

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 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 …

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

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 …

Abstract Solving Partially Observable Markov Decision Processes (POMDPs) with
continuous actions is challenging, particularly for high-dimensional action spaces. To …

Neuro-symbolic artificial intelligence is an emerging area that combines traditional symbolic
techniques with neural networks. In this paper, we consider its application to sequential …

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

Ecological management problems often involve navigating from an initial to a desired
community state. We ask whether navigation without brute‐force additions and deletions of …