  Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT)
are versatile in solving path planning problems. RRT* offers asymptotic
optimality but requires growing the tree uniformly over the free space, which
leaves room for efficiency improvement. To accelerate convergence, rule-based
informed approaches sample states in an admissible ellipsoidal subset of the
space determined by the current path cost. Learning-based alternatives model
the topology of the free space and infer the states close to the optimal path
to guide planning. We propose Neural Informed RRT* to combine the strengths
from both sides. We define point cloud representations of free states. We
perform Neural Focus, which constrains the point cloud within the admissible
ellipsoidal subset from Informed RRT*, and feeds into PointNet++ for refined
guidance state inference. In addition, we introduce Neural Connect to build
connectivity of the guidance state set and further boost performance in
challenging planning problems. Our method surpasses previous works in path
planning benchmarks while preserving probabilistic completeness and asymptotic
optimality. We deploy our method on a mobile robot and demonstrate real world
navigation around static obstacles and dynamic humans. Code is available at
https://github.com/tedhuang96/nirrt_star.
