4 accepted papers
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 a wide range of tasks. However; despite this empirical success; these controllers still lack theoretical guarantees…
Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optim…
Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid probl…
Quadrotor flight has typically been limited to sparse environments due to numerical complications that arise when dealing with large numbers of obstacles. We hypothesized that it would be possible to plan and robustly execute trajectories in obstacle-dense environments using the novel Iterative Regi