  This work investigates the use of Neural implicit representations,
specifically Neural Radiance Fields (NeRF), for geometrical queries and motion
planning. We show that by adding the capacity to infer occupancy in a radius to
a pre-trained NeRF, we are effectively learning an approximation to a Euclidean
Signed Distance Field (ESDF). Using backward differentiation of the augmented
network, we obtain an obstacle gradient that is integrated into an obstacle
avoidance policy based on the Riemannian Motion Policies (RMP) framework. Thus,
our findings allow for very fast sampling-free obstacle avoidance planning in
the implicit representation.
