Search Results for author:
Found 11 papers, 3 papers with code
A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly.
Motivating Physical Activity via Competitive Human-Robot Interaction
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)
Reachability Analysis of Neural Feedback Loops
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.
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers
Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties.
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems
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.
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections
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.
Learning Hierarchical Teaching Policies for Cooperative Agents
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers.
Context-Aware Pedestrian Motion Prediction In Urban Intersections
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.
A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
This paper presents a novel framework for accurate pedestrian intent prediction at intersections.
Transferable Pedestrian Motion Prediction Models at Intersections
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.
