  Planning for systems with dynamics is challenging as often there is no local
planner available and the only primitive to explore the state space is forward
propagation of controls. In this context, tree sampling-based planners have
been developed, some of which achieve asymptotic optimality by propagating
random controls during each iteration. While desirable for the analysis, random
controls result in slow convergence to high quality trajectories in practice.
  This short position statement first argues that if a kinodynamic planner has
access to local maneuvers that appropriately balance an
exploitation-exploration trade-off, the planner's per iteration performance is
significantly improved. Generating such maneuvers during planning can be
achieved by curating a large sample of random controls. This is, however,
computationally very expensive. If such maneuvers can be generated fast, the
planner's performance will also improve as a function of computation time.
  Towards objective, this short position statement argues for the integration
of modern machine learning frameworks with state-of-the-art, informed and
asymptotically optimal kinodynamic planners. The proposed approach involves
using using neural networks to infer local maneuvers for a robotic system with
dynamics, which properly balance the above exploitation-exploration trade-off.
In particular, a neural network architecture is proposed, which is trained to
reflect the choices of an online curation process, given local obstacle and
heuristic information. The planner uses these maneuvers to efficiently explore
the underlying state space, while still maintaining desirable properties.
Preliminary indications in simulated environments and systems are promising but
also point to certain challenges that motivate further research in this
direction.
