Bundled Gradients through Contact via Randomized Smoothing

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 me...

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Bibliographic Details
Published inarXiv.org
Main Authors Terry Suh, H J, Pang, Tao, Tedrake, Russ
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.01.2022
Subjects
Algorithms
Approximation
Empirical analysis
Fragility
Optimal control
Optimization
Smoothing
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Summary: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.
ISSN:2331-8422