I am working on planning and robot autonomy. I develop solvers for routing
problems and build robotics systems in simulation and the real world. Also, I
leverage vision language models to enhance decision-making.
I am actively seeking a Ph.D position in robotics, computer science, or
related fields starting in Fall 2027.
My research interests lie at the intersection of algorithmic robotics and
machine intelligence. I am dedicated to designing efficient search and
optimization-based algorithms, aiming to bridge the gap between classical
methods and modern learning-based approaches to achieve provably safe and
intelligent robot autonomy.
Introducing the multi-robot Hamiltonian Path Problem with Probabilistic
Terminals (MHPP-PT) to minimize the team's expected time to find a target,
with efficient optimal (MRPT*) and bounded suboptimal solvers (F-MRPT*).
Reformulating vision-language navigation as navigable pixel grounding, and
predicting a visible pixel in the image plane that is back-projected into a 3D
waypoint for navigation.
Introducing the Routing with Probabilistic Vertices (RPV) to model map
prediction uncertainty during exploration, with efficient optimal (RPV*) and
bounded suboptimal solvers (F-RPV*).
Considering the path planning problem for autonomous exploration of an unknown
environment using multiple heterogeneous robots and solving with search-based
method.
Combining probabilistic planning over predicted maps with online map
prediction, allowing the robot to adapt its exploration behavior as the
predicted map is refined.