  When the dynamics of a system are difficult to model and/or time-consuming to
evaluate, such as in deformable object manipulation tasks, motion planning
algorithms struggle to find feasible plans efficiently. Such problems are often
reduced to state spaces where the dynamics are straightforward to model and
evaluate. However, such reductions usually discard information about the system
for the benefit of computational efficiency, leading to cases where the true
and reduced dynamics disagree on the result of an action. This paper presents a
formulation for planning in reduced state spaces that uses a classifier to bias
the planner away from state-action pairs that are not reliably feasible under
the true dynamics. We present a method to generate and label data to train such
a classifier, as well as an application of our framework to rope manipulation,
where we use a Virtual Elastic Band (VEB) approximation to the true dynamics.
Our experiments with rope manipulation demonstrate that the classifier
significantly improves the success rate of our RRT-based planner in several
difficult scenarios which are designed to cause the VEB to produce incorrect
predictions in key parts of the environment.
