  Representing and reasoning about uncertainty is crucial for autonomous agents
acting in partially observable environments with noisy sensors. Partially
observable Markov decision processes (POMDPs) serve as a general framework for
representing problems in which uncertainty is an important factor. Online
sample-based POMDP methods have emerged as efficient approaches to solving
large POMDPs and have been shown to extend to continuous domains. However,
these solutions struggle to find long-horizon plans in problems with
significant uncertainty. Exploration heuristics can help guide planning, but
many real-world settings contain significant task-irrelevant uncertainty that
might distract from the task objective. In this paper, we propose STRUG, an
online POMDP solver capable of handling domains that require long-horizon
planning with significant task-relevant and task-irrelevant uncertainty. We
demonstrate our solution on several temporally extended versions of toy POMDP
problems as well as robotic manipulation of articulated objects using a neural
perception frontend to construct a distribution of possible models. Our results
show that STRUG outperforms the current sample-based online POMDP solvers on
several tasks.
