  The 3D shape of a robot's end-effector plays a critical role in determining
it's functionality and overall performance. Many industrial applications rely
on task-specific gripper designs to ensure the system's robustness and
accuracy. However, the process of manual hardware design is both costly and
time-consuming, and the quality of the resulting design is dependent on the
engineer's experience and domain expertise, which can easily be out-dated or
inaccurate. The goal of this work is to use machine learning algorithms to
automate the design of task-specific gripper fingers. We propose Fit2Form, a 3D
generative design framework that generates pairs of finger shapes to maximize
design objectives (i.e., grasp success, stability, and robustness) for target
grasp objects. We model the design objectives by training a Fitness network to
predict their values for pairs of gripper fingers and their corresponding grasp
objects. This Fitness network then provides supervision to a 3D Generative
network that produces a pair of 3D finger geometries for the target grasp
object. Our experiments demonstrate that the proposed 3D generative design
framework generates parallel jaw gripper finger shapes that achieve more stable
and robust grasps compared to other general-purpose and task-specific gripper
design algorithms. Video can be found at https://youtu.be/utKHP3qb1bg.
