no code implementations • 17 Sep 2023 • Maisha Maliha, Golnaz Habibi, Mohammed Atiquzzaman
The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly.
no code implementations • 14 Feb 2022 • Boling Yang, Golnaz Habibi, Patrick E. Lancaster, Byron Boots, Joshua R. Smith
This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 9 Aug 2021 • Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How
While the solutions are less tight than previous (semidefinite program-based) methods, they are substantially faster to compute, and some of those computational time savings can be used to refine the bounds through new input set partitioning techniques, which is shown to dramatically reduce the tightness gap.
1 code implementation • 5 Jan 2021 • Michael Everett, Golnaz Habibi, Jonathan P. How
Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties.
1 code implementation • 31 Oct 2020 • Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.
no code implementations • 1 Oct 2020 • Michael Everett, Golnaz Habibi, Jonathan P. How
Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs.
no code implementations • 21 Nov 2019 • 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 available.
no code implementations • 7 Mar 2019 • Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers.
no code implementations • 25 Jun 2018 • Nikita Jaipuria, Golnaz Habibi, Jonathan P. How
This paper presents a novel framework for accurate pedestrian intent prediction at intersections.
no code implementations • 25 Jun 2018 • Golnaz Habibi, Nikita Jaipuria, Jonathan P. How
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments.
no code implementations • 15 Mar 2018 • Macheng Shen, Golnaz Habibi, Jonathan P. How
We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection.