This year’s Penn Y-Prize competition featured novel technologies developed by members of the GRASP Lab, specifically the Sung Lab and ModLab.
We are thrilled to announce that the winner of this year’s Penn Y-Prize was Stentix! Their project, which utilized the MORF Technology developed in the Sung Lab, proposed a stent that can be adjusted noninvasively.
Congratulations to the entire Stentix team for your hard work and dedication!
Our recently submitted paper “MORF: Magnetic Origami Reprogamming and Folding System for Repeatably Reconfigurable Structures with Enhanced Fold Angle Control” has been accepted for publication at the 2025 IEEE International Conference on Robotics & Automation (2025 ICRA). Congratulations to the authors, Gabriel Unger, Sridhar Shenoy, Tianyu Li, Nadia Figueroa, and Cynthia Sung, for this exciting new research!
Our recently submitted paper “Effect of Jet Coordination on Underwater Propulsion with the Multi-Robot SALP System” has been accepted for publication at the 8th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2025). Congratulations to the authors, Zhiyuan Yang, Yipeng Zhang, Matthew Herbert, M. Ani Hsieh, and Cynthia Sung, for this exciting new research!
Congratulations to Daniel Feshbach and Ling Xu for presenting their innovative research at the Workshop on the Algorithmic Foundations of Robotics (WAFR) in Chicago!
Daniel’s work, “Algorithmic Design of Kinematic Trees Based on CSC Dubins Planning for Link Shapes,” introduces a new approach for designing kinematic trees using Dubins path for robotic structures. Ling’s presentation, “Reparametrization of 3D CSC Dubins’ Paths Enabling 2D Search,” simplifies 3D paths, enabling faster robot planning.
Both are making exciting contributions to robotics!
We held a workshop at the Arts League in West Philly to excite visitors about the Artists in Residency program with the Sung Robotics Lab. Attendees folded and assembled origami robots while learning about coding and mechanics.
Learn more about the Artists in Residency program:
https://bit.ly/3AZKYWe
