  Neural Motion Planners (NMPs) have emerged as a promising tool for solving
robot navigation tasks in complex environments. However, these methods often
require expert data for learning, which limits their application to scenarios
where data generation is time-consuming. Recent developments have also led to
physics-informed deep neural models capable of representing complex dynamical
Partial Differential Equations (PDEs). Inspired by these developments, we
propose Neural Time Fields (NTFields) for robot motion planning in cluttered
scenarios. Our framework represents a wave propagation model generating
continuous arrival time to find path solutions informed by a nonlinear
first-order PDE called Eikonal Equation. We evaluate our method in various
cluttered 3D environments, including the Gibson dataset, and demonstrate its
ability to solve motion planning problems for 4-DOF and 6-DOF robot
manipulators where the traditional grid-based Eikonal planners often face the
curse of dimensionality. Furthermore, the results show that our method exhibits
high success rates and significantly lower computational times than the
state-of-the-art methods, including NMPs that require training data from
classical planners.
