I am a Ph.D. student at GRASP Lab, University of Pennsylvania, advised by Prof. Nadia Figueroa. I envision a future where robots are not only capable, but also intuitive, adaptive, and safe. My research focuses on enabling robust and efficient motion policy learning, ensuring safe interactions between robots and their physical environments, and promoting engaging human-robot interactions. I am also passionate about designing innovative hardware.
Before coming to Penn, I received my MS in Robotics from Northwestern University and BS in Aerospace Engineering from UIUC.
Sep 2025 - I will be at Amazon Robotics working on Vulcan as an intern for Fall 2025.
Jun 2025 - EMP is accepted to IROS 2025.
Jun 2025 - OCR is accepted to IROS 2025.
May 2025 - VLMgineer is accepted to oral @ Workshop on Robot Hardware-Aware Intelligence, RSS 2025.
April 2025 - Presented as a spotlight speaker at the ICRA 2025 Doctoral Consortium.
April 2025 - EMP is accepted as a spotlight paper @ Workshop on Structured Learning, ICRA 2025.
Oct 2024 - OCR is accepted as a spotlight paper @ Workshop on Lifelong Learning for Home Robots, CoRL 2024.
Sep 2024 - Started an internship at Amazon Robotics, Innovation Lab (Westborough, MA).
Under Review [website]
We introduce a framework for generating force-informed data in simulation, instantiated by a single human demonstration, and show how coupling with a compliant policy improves the performance of a visuomotor policy learned from synthetic data.
Under Review [website]
A quadratic programming-based control framework enforces viability theory constructed constraints on a passive controller tracking a dynamical system, ensuring the robot states remain within the safe set in an infinite time horizon.
Workshop on Robot Hardware-Aware Intelligence, RSS 2025 [website]
We introduce VLMgineer, a novel VLM-driven evolutionary framework that automatically co-design tools and actions to solve robotics task.
Spotlight Paper in Workshop on Structured Learning for Efficient, Reliable, and Transparent Robots @ ICRA 2025
A one-shot stable imitation learning framework that allows robots to adjust their behavior based on the scene change while respecting the task specification.
ICRA 2025 [website]
We introduce MORF, a magnetic origami system enabling repeatedly reprogrammable, rigid structures ideal for adaptive robotic tools.
Spotlight Paper in Workshop on Lifelong Learning for Home Robots @ CoRL 2024
A vision-based object-centric recovery policy framework that guides the system back to the training distribution in OOD situations without relying on extra data
ICRA 2024 [website]
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
RSS 2024 [website]
A dynamical system based representation for intent estimation with constraints in human robot co-manipulation tasks
CoRL 2023 [website]
A dynamical system based motion policy LfD method with stability guarantees that can generalize to new task configurations without new demonstrations
RA-L [website]
A novel statistical model that applies the Riemannian metric on the n-sphere to efficiently blend non-Euclidean directional data with Euclidean states for learning stable, time-independent motion policies
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
Performed receding horizon control using data-driven approach with small data in a continuous space