微分可能シミュレーション最適化の大域的視点からの再考
Rethinking Optimization with Differentiable Simulation from a Global Perspective
接触や変形を含む複雑な系では微分可能シミュレーションの勾配が荒れた地形になり単一降下では大域最適に届かない問題に対し、ベイズ最適化と半局所的な「跳躍」を組み合わせて勾配を活かしつつノイズに頑健な大域探索法を提案した。
著者: Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula, Jeannette Bohg
分類: stat.ML, cs.LG, cs.RO
原文アブストラクト
Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification. However, existing approaches to differentiable simulation have largely tackled scenarios where obtaining smooth gradients has been relatively easy, such as systems with mostly smooth dynamics. In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios. We analyze the optimization landscapes of diverse scenarios that contain both rigid bodies and deformable objects. In dynamic environments with highly deformable objects and fluids, differentiable simulators produce rugged landscapes with nonetheless useful gradients in some parts of the space. We propose a method that combines Bayesian optimization with semi-local 'leaps' to obtain a global search method that can use gradients effectively, while also maintaining robust performance in regions with noisy gradients. We show that our approach outperforms several gradient-based and gradient-free baselines on an extensive set of experiments in simulation, and also validate the method using experiments with a real robot and deformables. Videos and supplementary materials are available at https://tinyurl.com/globdiff
