My research lies at the intersection between game theory and trajectory optimization for robotic systems.
The main applications of this work are planning for autonomous vehicles and robust control.
We coupled an online estimation technique and a fast dynamic game solver to allow an autonomous driving vehicle to optimize its trajectory while learning the objective functions of the cars in its surroundings. It allows the autonomous vehicle to quickly identify the desired speed, desired lane and aggresiveness level of the vehicles it is interacting with. This gained information further improves the trajectory prediction ability of the autonomous vehicle.
We developed an algorithm relying on a novel sampling-based technique to efficiently compute regions of finite time invariance, commonly refered to as “invariant funnels”. We apply our algorithm to a spacecraft atmospheric entry problem where the goal is to identify the landing zone that the spacecraft is guaranteed to reach under uncertain atmospheric disturbances.
We developed fast solver for constrained dynamic games and applied it to complex autonomous driving scenarios. It generates complex driving behaviors where vehicles negotiate and share the responsibility for avoiding collisions.
We applied the Alternating Direction Method of Multipliers (ADMM) to solve optimal control problems with L1-norm cost. One application of this work is minimum-fuel trajectory optimization for satellites.
Find my resume and CV.
Find my publications and the related materials: paper, video, code, slides, poster, etc.
Code that I wrote for my research projects.
Slides and videos presented during conference.
List of courses for which I've been teaching assistant.
[my first name] [lc] [at] stanford [dot] edu.