In recent years, semidefinite relaxations of common optimization problems in robotics have
attracted growing attention due to their ability to provide globally optimal solutions. In many …

We propose semidefinite trajectory optimization (STROM), a framework that computes fast
and certifiably optimal solutions for nonconvex trajectory optimization problems defined by …

This thesis introduces a new class of problems at the interface of combinatorial and convex
optimization. We consider graphs where each vertex is paired with a convex program, and …

We consider large-scale, implicit-search-based solutions to Shortest Path Problems on
Graphs of Convex Sets (GCS). We propose GCS*, a forward heuristic search algorithm that …

The Shortest-Path Problem in Graph of Convex Sets (SPP in GCS) is a recently developed
optimization framework that blends discrete and continuous decision making. Many relevant …

Behavior cloning (BC) traditionally relies on demonstration data, assuming the
demonstrated actions are optimal. This can lead to overfitting under noisy data, particularly …

Collision-free motion planning with trajectory optimization is inherently nonconvex. Some of
this nonconvexity is fundamental: the robot might need to make a discrete decision to go left …

Collision-free motion planning with trajectory optimization is inherently nonconvex. Some of
this nonconvexity is fundamental: the robot might need to make a discrete decision to go left …

Humans can exploit contacts anywhere on their body surface to manipulate large and heavy
items, objects normally out of reach or multiple objects at once. However, such manipulation …

The tremendous success of behavior cloning (BC) in robotic manipulation has been largely
confined to tasks where demonstrations can be effectively collected through human …