I am a Ph.D. student at GRASP Lab, University of Pennsylvania, advised by Prof. Nadia Figueroa. My research mainly focuses on safe and efficient motion policy learning for physical human robot interaction. Iām constantly exploring how robots can assist, collaborate, and interact with humans in various capacities. This endeavor involves methods from machine learning, control theory, and perception, aiming to bridge the gap between robot capabilities and human needs.
Before coming to Penn, I received my MS in Robotics from Northwestern University and BS in Aerospace Engineering from UIUC.
CoRL 2023 [website]
A dynamical system based motion policy LfD method with stability guarantees that can generalize to new task configurations without new demonstrations
ICRA 2024 [website available soon]
A novel control architecture that allows a torque-controlled robot to guarantee safety constraints such as kinematic limits, self-collisions, external collisions and singularities and is passive only when feasible
ICRA 2024 [website]
This approach learns a motion plan with Neural Ordinary Differential Equations while guaranteeing stability and safety with Control Lyapunov Functions and Control Barrier Functions
Using maximum entropy deep IRL to learn agent preference in continuous environment path planning
Developed software and hardware pipeline to play yoyo with visual feedback control on the Sawyer Robot arm
Model predictive control with the ability to match or avoid distributions
Implementation of landmark-based EKF-SLAM with unsupervised learning and unknown data association using ROS in C++ from scratch.
Performed receding horizon control using data-driven approach with small data in a continuous space