  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.

Robotics;Systems and Control;Systems and Control