Abstract
Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among other techniques. In this paper, we present a stochastic optimization technique with chance constraints for systems with stochastic complementarity constraints. We use a particle filter-based approach to propagate moments for stochastic complementarity system. To circumvent the issues of open-loop chance constrained planning, we propose a contact-aware controller for covariance steering of the complementarity system. Our optimization problem is formulated as Non-Linear Programming (NLP) using bilevel optimization. We present an important-particle algorithm for numerical efficiency for the underlying control problem. We verify that our contact-aware closed-loop controller is able to steer the covariance of the states under stochastic contact-rich tasks.
Abstract (translated)
对不确定接触系统的规划和控制是挑战性的，因为不清楚如何传播不确定性用于规划。接触丰富的任务可以通过互补约束其他技巧高效建模。在本文中，我们提出了一种随机优化技巧，具有随机机会约束的系统。我们使用粒子滤波方法传播 Moments 对随机互补系统。为了绕过开放循环机会约束规划的问题，我们提出了一个接触aware控制器，用于covariance 指导互补系统。我们的优化问题使用双水平优化提出了一个重要的粒子算法，用于提高底层控制问题的数值效率。我们验证，我们的接触aware闭环控制器能够在接触丰富的任务下指导状态covariance。
URL
https://arxiv.org/abs/2303.13382
