  In this work, we aim to teach robots to manipulate various thin-shell
materials. Prior works studying thin-shell object manipulation mostly rely on
heuristic policies or learn policies from real-world video demonstrations, and
only focus on limited material types and tasks (e.g., cloth unfolding).
However, these approaches face significant challenges when extended to a wider
variety of thin-shell materials and a diverse range of tasks. While virtual
simulations are shown to be effective in diverse robot skill learning and
evaluation, prior thin-shell simulation environments only support a subset of
thin-shell materials, which also limits their supported range of tasks. We
introduce ThinShellLab - a fully differentiable simulation platform tailored
for robotic interactions with diverse thin-shell materials possessing varying
material properties, enabling flexible thin-shell manipulation skill learning
and evaluation. Our experiments suggest that manipulating thin-shell objects
presents several unique challenges: 1) thin-shell manipulation relies heavily
on frictional forces due to the objects' co-dimensional nature, 2) the
materials being manipulated are highly sensitive to minimal variations in
interaction actions, and 3) the constant and frequent alteration in contact
pairs makes trajectory optimization methods susceptible to local optima, and
neither standard reinforcement learning algorithms nor trajectory optimization
methods (either gradient-based or gradient-free) are able to solve the tasks
alone. To overcome these challenges, we present an optimization scheme that
couples sampling-based trajectory optimization and gradient-based optimization,
boosting both learning efficiency and converged performance across various
proposed tasks. In addition, the differentiable nature of our platform
facilitates a smooth sim-to-real transition.
