Nathan P. Lawrence — accepted AI-conference papers

2026

ARMOR: Attack-Resilient Reinforcement Learning Control for UAVs

Pritam Dash, Ethan Chan, Nathan P. Lawrence, Karthik Pattabiraman

ICRA 2026poster

Unmanned Aerial Vehicles (UAVs) depend on onboard sensors for perception, navigation, and control. However, these sensors are susceptible to physical attacks, such as GPS spoofing, that can corrupt state estimates and lead to unsafe behavior. While reinforcement learning (RL) offers adaptive control…

Cited by 0 Scholar ↗

2024

Guiding Reinforcement Learning with Incomplete System Dynamics

Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni +1

IROS 2024poster

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, an…

Cited by 2 Source ↗Scholar ↗