Common methods for learning robot dynamics assume motion is continuous, causing
unrealistic model predictions for systems undergoing discontinuous impact and stiction …

In this article, we present a general approach for controlling robotic systems that make and
break contact with their environments. Contact-implicit model predictive control (CI-MPC) …

We propose a control framework, which can utilize tactile information by exploiting the
complementarity structure of contact dynamics. Since many robotic tasks, like manipulation …

Contact adaptation is an essential capability when manipulating objects. Two key contact
modes of non-prehensile manipulation are sticking and sliding. This paper presents a …

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 …

Given a desired object trajectory, how should a robot make contact to achieve it? This paper
proposes a global optimization model for this problem with alternated-sticking contact …

This article investigates one of the most challenging tasks in dynamic manipulation—
catching large-momentum moving objects. Beyond the realm of quasi-static manipulation …

Mixed‐integer model predictive control (MI‐MPC) can be a powerful tool for controlling
hybrid systems. In case of a linear‐quadratic objective in combination with linear or …

Differentiable physics-based simulators have witnessed remarkable success in robot
learning involving contact dynamics, benefiting from their improved accuracy and efficiency …

While mixed-integer convex programs (MICPs) arise frequently in mixed-integer optimal
control problems (MIOCPs), current state-of-the-art MICP solvers are often too slow for real …