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.
翻译:规划和控制不确定接触系统具有挑战性,因为不清楚如何为规划传播不确定性。接触丰富任务可以使用互补约束等技术有效地建模。在本文中,我们提出了一种具有机会约束的随机优化技术,用于具有随机互补性约束的系统。我们使用基于粒子滤波器的方法传播随机互补系统的统计量。为了避开开环机会约束规划的问题,我们提出了一个针对协方差导引互补系统的接触感知控制器。我们的优化问题使用双层优化来制定为非线性规划(NLP)。我们提出了一种重要的粒子算法以实现底层控制问题的数值效率。我们验证了我们的接触感知闭环控制器能够在随机接触丰富任务下引导状态的协方差。