Search Results for author:
Found 11 papers, 2 papers with code
Challenges Faced by Large Language Models in Solving Multi-Agent Flocking
Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation.
Dynamic Adversarial Attacks on Autonomous Driving Systems
Moreover, our method utilizes dynamic patches displayed on a screen, allowing for adaptive changes and movement, enhancing the flexibility and performance of the attack.
Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network
However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centralized control policy.
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense
In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders.
An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA
We compare the different results achieved with the FPGA and GPU-based implementations and then discuss the pros and cons of each implementation.
Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks
We consider the problem of finding decentralized strategies for multi-agent perimeter defense games.
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation.
Robotics
Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm.
An Approximation Algorithm for Risk-averse Submodular Optimization
We formulate a discrete submodular maximization problem for selecting a set using Conditional-Value-at-Risk (CVaR), a risk metric commonly used in financial analysis.
