  The pipeline of current robotic pick-and-place methods typically consists of
several stages: grasp pose detection, finding inverse kinematic solutions for
the detected poses, planning a collision-free trajectory, and then executing
the open-loop trajectory to the grasp pose with a low-level tracking
controller. While these grasping methods have shown good performance on
grasping static objects on a table-top, the problem of grasping dynamic objects
in constrained environments remains an open problem. We present Neural Motion
Fields, a novel object representation which encodes both object point clouds
and the relative task trajectories as an implicit value function parameterized
by a neural network. This object-centric representation models a continuous
distribution over the SE(3) space and allows us to perform grasping reactively
by leveraging sampling-based MPC to optimize this value function.
