240 citations · 364 across the 22 of their papers we have counts for
3 papers · 2 filters
Certified Adversarial Robustness for Deep Reinforcement Learning
Björn Lütjens, Michael Everett, Jonathan P. How
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees…
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Michael Everett, Yu Fan Chen, Jonathan P. How
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians. This work proposes using deep reinforcement (RL) learning as a framework to m…
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments
Jesus Tordesillas, Brett T. Lopez, Jonathan P. How
High-speed trajectory planning through unknown environments requires algorithmic techniques that enable fast reaction times while maintaining safety as new information about the op…