So-called robot–object Lagrangian systems consist of a class of nonsmooth underactuated
complementarity Lagrangian systems, with a specific structure: an “object” and a “robot” …

We propose a hybrid model predictive control algorithm, consensus complementarity
control, for systems that make and break contact with their environment. Many state-of-the …

We propose a hybrid model predictive control algorithm, consensus complementarity control
(C3), for systems that make and break contact with their environment. Many state-of-the-art …

We present a general, two-stage reinforcement learning approach to create robust policies
that can be deployed on real robots without any additional training using a single …

PyROBOCOP is a Python-based package for control, optimization and estimation of robotic
systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the …

This paper investigates the learning, or system identification, of a class of piecewise-affine
dynamical systems known as linear complementarity systems (LCSs). We propose a …

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

Estimating the velocity of a mass involved in nonsmooth impacts is a challenging problem
because of their hybrid nature, especially when the restitution coefficient and the orientation …

Contacts are central to most manipulation tasks as they provide additional dexterity to robots
to perform challenging tasks. However, frictional contacts leads to complex complementarity …

Smoothed online learning has emerged as a popular framework to mitigate the substantial
loss in statistical and computational complexity that arises when one moves from classical to …