  When humans perform contact-rich manipulation tasks, customized tools are
often necessary to simplify the task. For instance, we use various utensils for
handling food, such as knives, forks and spoons. Similarly, robots may benefit
from specialized tools that enable them to more easily complete a variety of
tasks. We present an end-to-end framework to automatically learn tool
morphology for contact-rich manipulation tasks by leveraging differentiable
physics simulators. Previous work relied on manually constructed priors
requiring detailed specification of a 3D object model, grasp pose and task
description to facilitate the search or optimization process. Our approach only
requires defining the objective with respect to task performance and enables
learning a robust morphology through randomizing variations of the task. We
make this optimization tractable by casting it as a continual learning problem.
We demonstrate the effectiveness of our method for designing new tools in
several scenarios, such as winding ropes, flipping a box and pushing peas onto
a scoop in simulation. Additionally, experiments with real robots show that the
tool shapes discovered by our method help them succeed in these scenarios.
