Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance,
and planning of the underactuated dynamics of the system. Reliably optimizing for such …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

The controller design for the robotic manipulator faces different challenges such as the
system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of …

We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-
time autonomous hybrid systems that admit a mixed-integer representation. Such systems …

The polytope containment problem is deciding whether a polytope is a contained within
another polytope. The complexity heavily depends on how the polytopes are represented …

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 …

While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …

We present a novel method for global motion planning of robotic systems that interact with
the environment through contacts. Our method directly handles the hybrid nature of such …

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 …

Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to
sensor observations. In contrast, robots can not perform reactive manipulation and they …