Abstract Space mission design places a premium on cost and operational efficiency. The
search for new science and life beyond Earth calls for spacecraft that can deliver scientific …

From quadrotors delivering packages in urban areas to robot arms moving in confined
warehouses, motion planning around obstacles is a core challenge in modern robotics …

Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement
learning algorithms have been highly effective in synthesizing neural-network controllers for …

Learning predictive models from observations using deep neural networks (DNNs) is a
promising new approach to many real-world planning and control problems. However …

Given a graph, the shortest-path problem requires finding a sequence of edges with
minimum cumulative length that connects a source vertex to a target vertex. We consider a …

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 …

The empirical success of derivative-free methods in reinforcement learning for planning
through contact seems at odds with the perceived fragility of classical gradient-based …

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 …

In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is
solved at each sampling time to compute the optimal control action. Although these …