  We present a method for differentiable simulation of soft articulated bodies.
Our work enables the integration of differentiable physical dynamics into
gradient-based pipelines. We develop a top-down matrix assembly algorithm
within Projective Dynamics and derive a generalized dry friction model for soft
continuum using a new matrix splitting strategy. We derive a differentiable
control framework for soft articulated bodies driven by muscles, joint torques,
or pneumatic tubes. The experiments demonstrate that our designs make soft body
simulation more stable and realistic compared to other frameworks. Our method
accelerates the solution of system identification problems by more than an
order of magnitude, and enables efficient gradient-based learning of motion
control with soft robots.
