  Robotic pick and place stands at the heart of autonomous manipulation. When
conducted in cluttered or complex environments robots must jointly reason about
the selected grasp and desired placement locations to ensure success. While
several works have examined this joint pick-and-place problem, none have fully
leveraged recent learning-based approaches for multi-fingered grasp planning.
We present a modular algorithm for joint pick and place planning that can make
use of state of the art grasp classifiers for planning multi-fingered grasps
for novel objects from partial view point clouds. We demonstrate our joint pick
and place formulation with several costs associated with different placement
tasks. Experiments on pick and place tasks with cluttered scenes using a
physical robot show that our joint inference method is more successful than a
sequential pick then place approach, while also achieving better placement
configurations.
