4 accepted papers
The metareasoning framework aims to enable autonomous agents to factor in planning costs when making decisions. In this work, we develop the first non-myopic metareasoning algorithm for planning with Markov decision processes. Our method learns the behaviour of anytime probabilistic planning algorit…
We consider planning for mobile robots conducting missions in real-world domains where a priori unknown dynamics affect the robot’s costs and transitions. We study single-episode missions where it is crucial that the robot appropriately trades off exploration and exploitation, such that the learning…
Harvesting valuable ocean data, ranging from climate and marine life analysis to industrial equipment monitoring, is an extremely challenging real-world problem. Sparse underwater sensor networks are a promising approach to scale to larger and deeper environments, but these have difficulty offloadin…
When exploring an unknown environment, a mobile robot must decide where to observe next. It must do this whilst minimising the risk of failure, by only exploring areas that it expects to be safe. In this context, safety refers to the robot remaining in regions where critical environment features (e.…