  Grasping in dynamic environments presents a unique set of challenges. A
stable and reachable grasp can become unreachable and unstable as the target
object moves, motion planning needs to be adaptive and in real time, the delay
in computation makes prediction necessary. In this paper, we present a dynamic
grasping framework that is reachability-aware and motion-aware. Specifically,
we model the reachability space of the robot using a signed distance field
which enables us to quickly screen unreachable grasps. Also, we train a neural
network to predict the grasp quality conditioned on the current motion of the
target. Using these as ranking functions, we quickly filter a large grasp
database to a few grasps in real time. In addition, we present a seeding
approach for arm motion generation that utilizes solution from previous time
step. This quickly generates a new arm trajectory that is close to the previous
plan and prevents fluctuation. We implement a recurrent neural network (RNN)
for modelling and predicting the object motion. Our extensive experiments
demonstrate the importance of each of these components and we validate our
pipeline on a real robot.
