publications

2025

1. 

   Practical and Performant Enhancements for Maximization of Algebraic Connectivity

   Leonard Jung, Alan Papalia, Kevin Doherty, and 1 more author

   arXiv preprint arXiv:2511.08694, 2025

   arXiv

2. 

   Verification of Visual Controllers via Compositional Geometric Transformations

   Alexander Estornell, Leonard Jung, and Michael Everett

   arXiv preprint arXiv:2507.04523, 2025

   arXiv
3. Learning Smooth State-Dependent Traversability from Dense Point Clouds

   Zihao Dong, Alan Papalia, Leonard Jung, and 4 more authors

   In RSS 2025 Workshop on Resilient Off-road Autonomous Robotics, 2025

   arXiv
4. LiDAR Inertial Odometry and Mapping Using Learned Registration-Relevant Features

   Zihao Dong, Jeff Pflueger, Leonard Jung, and 5 more authors

   In 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025

   DOI arXiv

5. 

   A Hybrid Framework for Efficient Koopman Operator Learning

   Alexander Estornell^*, Leonard Jung^*, Alenna Spiro^*, and 2 more authors

   arXiv preprint arXiv:2504.18676, 2025

   arXiv

6. 

   Contingency Constrained Planning with MPPI within MPPI

   Leonard Jung, Alexander Estornell, and Michael Everett

   In Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 04–06 jun 2025

   Abs arXiv PDF

   For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method’s sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot.

2023

1. An Origami-Inspired Deployable Space Debris Collector

   Yuto Tanaka, Aditya Arjun Anibha, Leonard Jung, and 3 more authors

   In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 04–06 jun 2023

   DOI HTML