Abstract:

Robots are increasingly deployed in settings where safety, performance, or both are mission-critical—from agile aerial vehicles avoiding collisions at high speed to manipulators executing intricate, contact-rich tasks. In my thesis, I present a unifying approach to these seemingly disparate challenges through the lens of reachable sets, a versatile but underutilized computational primitive in robotics.

In the first part, I focus on reactive safe control for agile systems, such as drones, extending reachability-based methods to handle uncertain, high-dimensional, real-world systems. I will describe two approaches: a robust-adaptive controller synthesized via sum-of-squares programming, which guarantees safety while minimizing interference with performance, and a neural-network-based control barrier function learned adversarially for scalability without sacrificing safety.

In the second part, I turn to motion planning for contact-rich manipulation, where the challenge lies in the hybrid nature of the problem—combinatorial contact modes coupled with continuous, nonconvex motion. Here, I introduce a hierarchical planner that uses reachable sets as motion primitives, constructed in object space to capture kinematic and dynamic feasibility by design. This enables efficient global planning for complex bimanual systems, producing significantly higher-quality trajectories than state-of-the-art methods. Together, these contributions demonstrate the breadth of problems reachable sets can address, from low-level reactive safety to long-horizon planning.

Committee Members: