Andrew Sang-Jin Choi

Hi! Thanks for visiting my website ^_^

Big Update: I recently defended my thesis titled "Simulation of Deformable Objects for Sim2Real Applications in Robotics"! 🥳🥳🥳 I am now starting a new role as a research scientist on the General AI team at Horizon Robotics.

I obtained my Ph.D. in computer science at UCLA, where I was a member of the Structures-Computer Interaction (SCI) Lab. I was co-advised by Professors M. Khalid Jawed, Jungseock Joo, and Demetri Terzopoulos. Before that, I obtained a B.S. in mechanical engineering at UC Davis and an M.S. in computer science at UCLA.

During my Ph.D., my research was quite multidisciplinary, involving fields such as robotics, simulation, perception, and deep learning. A key focus during my studies was the sim2real gap. I was especially interested in leveraging physics-based simulations to teach useful and transferable skills to robots through data-driven methods.

Email  /  CV  /  LinkedIn  /  Google Scholar  /  GitHub

End-to-end pipeline for full sim2real robotic paper folding. Neural force manifolds are learned from physical simulation and are used to generate optimal folding trajectories. Scaling analysis is used to obtain a generalizable control policy with respect to material and geometric parameters.

Full scale generalizable physical simulator capable of simulating soft physics, frictional contact, and continuum control. A fully implicit formulation allows for increased computational efficiency while maintaining physical accuracy when compared to previous SOTA.

End-to-end pipeline for full sim2real robotic DLO deployment. Physical simulations are used to train a neural controller capable of deploying rods along a prescribed pattern. Scaling analysis is used to obtain a generalizable control policy with respect to material and geometric parameters.

Robust and rapid instance segmentation of DLOs via skeleton pixel traversals. Segmentations are generated on topologically corrected skeleton structures with intersections handled by the simple and physically insightful optimization objective of minimizing cumulative bending energy.

Fully implicit frictional contact method for simulation of slender elastic rods. Enforces non-penetration and capable of reproducing sticking and sliding phenomena. Case study for flagella bundling in viscous fluid shown.

Study of the snap buckling process of overhand knots. Discrete differential geometry based simulations and tabletop experiments are used to explore the onset of buckling as a function of topology, geometry, and friction.

Preemptive motion planning via human object placement prediction using human pose and gaze as input. Rapidly outperforms traditional wait-and-react methods for human-to-robot pick-and-place operations.

Weak supervision approaches for automated vessel segmentation. Utilizes active contour models to generate weak labels and introduces low-cost human-in-the-loop strategies that drastically reduce labelling cost while maintaining segmentation accuracy.

An implicit contact model for slender elastic rod simulation capable of enforcing non-penetration via smooth penalty energy and edge-to-edge minimum distance formulation. Sliding friction is handled semi-explicitly. Extensive physical validation through knot tying case study.

Implementation of the paper "Hand-Eye Calibration of SCARA Robots" by M. Ulrich. Completed as part of an internship at Vecna Robotics.

ROS wrapper for OpenPose with support for key commercial cameras used by the robotics community.

An Arduino controlled robot dog that I created while healing from a knee surgery. Can do simple functionalities such as sit and respond to praise. So far, the YouTube video has amassed over 117000 views!

A manipulator capable of tying shoes. Built under the design constraints of using at most 2 motors and a $600 budget. Won a robotics competition against Meijo University, resulting in an all expense paid trip to Japan to present the robot. Was featured on several news outlets. So far, the YouTube video has amassed over 55000 views!

Website source code from Jon Barron.