ランダム化平滑化による微分可能物理の拡張
Augmenting differentiable physics with randomized smoothing
微分可能物理の勾配が不連続・非凸な場合に、ランダム化平滑化で近傍勾配を推定し最適化を改善する手法を提案。メッシュ再構成や接触・摩擦を含むロボット制御で有効性を示した。
著者: Quentin Le Lidec, Louis Montaut, Cordelia Schmid, Ivan Laptev, Justin Carpentier
分類: cs.RO
原文アブストラクト
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.
