ランダム化平滑化による接触を介したバンドル勾配
Bundled Gradients through Contact via Randomized Smoothing
接触を含む非平滑な力学系において、ランダム化平滑化を用いて勾配のサンプリング近似（勾配バンドル）を定式化し、iLQRと組み合わせることでマニピュレーションの接触計画問題の収束を改善した論文。
著者: H. J. Terry Suh, Tao Pang, Russ Tedrake
分類: cs.RO
原文アブストラクト
The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optimization methods in these domains. What is causing this gap, and how might we use the answer to improve gradient-based methods? We believe a stochastic formulation of dynamics is one crucial ingredient. We use tools from randomized smoothing to analyze sampling-based approximations of the gradient, and formalize such approximations through the gradient bundle. We show that using the gradient bundle in lieu of the gradient mitigates fast-changing gradients of non-smooth contact dynamics modeled by the implicit time-stepping, or the penalty method. Finally, we apply the gradient bundle to optimal control using iLQR, introducing a novel algorithm which improves convergence over using exact gradients. Combining our algorithm with a convex implicit time-stepping formulation of contact, we show that we can tractably tackle planning-through-contact problems in manipulation.
