64 citations · 105 across the 36 of their papers we have counts for
18 papers · 1 filter
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections
Golnaz Habibi, Nikita Japuria, Jonathan P. How
This paper presents a novel incremental learning algorithm for pedestrian motion prediction, with the ability to improve the learned model over time when data is incrementally avai…
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…
Dynamic Landing of an Autonomous Quadrotor on a Moving Platform in Turbulent Wind Conditions
Aleix Paris, Brett T. Lopez, Jonathan P. How
Autonomous landing on a moving platform presents unique challenges for multirotor vehicles, including the need to accurately localize the platform, fast trajectory planning, and pr…
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games
Macheng Shen, Jonathan P. How
This paper presents an algorithmic framework for learning robust policies in asymmetric imperfect-information games, where the joint reward could depend on the uncertain opponent t…
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
Arpan Kusari, Jonathan P. How
A common approach for defining a reward function for Multi-objective Reinforcement Learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then tre…