Augmenting differentiable physics with randomized smoothing

06/23/2022
by   Quentin Le Lidec, et al.
0

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.

READ FULL TEXT
research
07/08/2022

Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?

In recent years, an increasing amount of work has focused on differentia...
research
11/10/2021

Gradients are Not All You Need

Differentiable programming techniques are widely used in the community a...
research
04/28/2023

Improving Gradient Computation for Differentiable Physics Simulation with Contacts

Differentiable simulation enables gradients to be back-propagated throug...
research
01/30/2022

A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques

Metaheuristic algorithms are methods devised to efficiently solve comput...
research
10/18/2021

Differentiable Rendering with Perturbed Optimizers

Reasoning about 3D scenes from their 2D image projections is one of the ...
research
09/11/2021

Bundled Gradients through Contact via Randomized Smoothing

The empirical success of derivative-free methods in reinforcement learni...
research
04/14/2022

Accelerated Policy Learning with Parallel Differentiable Simulation

Deep reinforcement learning can generate complex control policies, but r...

Please sign up or login with your details

Forgot password? Click here to reset