  Solving partially observable Markov decision processes (POMDPs) with high
dimensional and continuous observations, such as camera images, is required for
many real life robotics and planning problems. Recent researches suggested
machine learned probabilistic models as observation models, but their use is
currently too computationally expensive for online deployment. We deal with the
question of what would be the implication of using simplified observation
models for planning, while retaining formal guarantees on the quality of the
solution. Our main contribution is a novel probabilistic bound based on a
statistical total variation distance of the simplified model. We show that it
bounds the theoretical POMDP value w.r.t. original model, from the empirical
planned value with the simplified model, by generalizing recent results of
particle-belief MDP concentration bounds. Our calculations can be separated
into offline and online parts, and we arrive at formal guarantees without
having to access the costly model at all during planning, which is also a novel
result. Finally, we demonstrate in simulation how to integrate the bound into
the routine of an existing continuous online POMDP solver.
