53 citations · 114 across the 11 of their papers we have counts for
12 papers
Multi-Agent Spectrum Sharing
Job Elliott, Graduate Student Member, IEEE +2
This project explores how multiple cognitive radars can learn to share limited wireless spectrum with other radio users without interfering with one another. Using machine learning…
Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT
Luca Carlone, Kasra Khosoussi, Vasileios Tzoumas +5
This paper reports on the development, execution, and open-sourcing of a new robotics course at MIT. The course is a modern take on "Visual Navigation for Autonomous Vehicles" (VNA…
Motivating Physical Activity via Competitive Human-Robot Interaction
Boling Yang, Golnaz Habibi, Patrick E. Lancaster +2
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 ex…
Reachability Analysis of Neural Feedback Loops
Michael Everett, Golnaz Habibi, Chuangchuang Sun +1
Neural Networks (NNs) can provide major empirical performance improvements for closed-loop systems, but they also introduce challenges in formally analyzing those systems' safety p…
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers
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 prope…
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Dong-Ki Kim, Miao Liu, Matthew Riemer +6
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each…
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems
Michael Everett, Golnaz Habibi, Jonathan P. How
Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain environments, but the tools for formally analyzing how this uncertainty propagates to…
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…
Learning Hierarchical Teaching Policies for Cooperative Agents
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei +7
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teamma…
Context-Aware Pedestrian Motion Prediction In Urban Intersections
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 n…
A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
Nikita Jaipuria, Golnaz Habibi, Jonathan P. How
This paper presents a novel framework for accurate pedestrian intent prediction at intersections. Given some prior knowledge of the curbside geometry, the presented framework can a…
Transferable Pedestrian Motion Prediction Models at Intersections
Macheng Shen, Golnaz Habibi, Jonathan P. How
One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in devel…