Eric Lepowsky1,2,†, David Snyder1,3,†, Alexander Glaser1,2, Anirudha Majumdar1,3
1Mechanical and Aerospace Engineering, Princeton University, NJ, USA.
2Program on Science and Global Security (SGS), Princeton, NJ, USA.
3Intelligent Robot Motion (IRoM) Lab, Princeton, NJ, USA.
†E. L. and D. S. contributed equally; other authors listed alphabetically.
Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially secret nature of observable features, we propose a robotic verification procedure that provides map-free exploration to perform a source verification task without requiring, nor revealing, any task-irrelevant, site-specific information. We provide theoretical guarantees on the privacy and correctness of our approach, validated by extensive simulated and hardware experiments.
The full paper is available here: arXiv:2402.17130
This repository is built from the following sources:
-
E. Coumans and Y. Bai. PyBullet, a Python module for physics simulation for games, robotics and machine learning. 2021.
-
B. Ellenberger. PyBullet Gymperium. 2019.
-
Y. Kadhi et al. Learning and generalization on a navigation task of a wheeled robot. 2021.