  In the past few years, following the differentiable programming paradigm,
there has been a growing interest in computing the gradient information of
physical processes (e.g., physical simulation, image rendering). However, such
processes may be non-differentiable or yield uninformative gradients (i.d.,
null almost everywhere). When faced with the former pitfalls, gradients
estimated via analytical expression or numerical techniques such as automatic
differentiation and finite differences, make classical optimization schemes
converge towards poor quality solutions. Thus, relying only on the local
information provided by these gradients is often not sufficient to solve
advanced optimization problems involving such physical processes, notably when
they are subject to non-smoothness and non-convexity issues.In this work,
inspired by the field of zero-th order optimization, we leverage randomized
smoothing to augment differentiable physics by estimating gradients in a
neighborhood. Our experiments suggest that integrating this approach inside
optimization algorithms may be fruitful for tasks as varied as mesh
reconstruction from images or optimal control of robotic systems subject to
contact and friction issues.
