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The pipeline of current robotic pick-and-place methods typically consists ofseveral stages: grasp pose detection, finding inverse kinematic solutions forthe detected poses, planning a collision-free trajectory, and then executingthe open-loop trajectory to the grasp pose with a low-level trackingcontroller. While these grasping methods have shown good performance ongrasping static objects on a table-top, the problem of grasping dynamic objectsin constrained environments remains an open problem. We present Neural MotionFields, a novel object representation which encodes both object point cloudsand the relative task trajectories as an implicit value function parameterizedby a neural network. This object-centric representation models a continuousdistribution over the SE(3) space and allows us to perform grasping reactivelyby leveraging sampling-based MPC to optimize this value function.

Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions