Dario Pompili

LLM-Based Multi-Agent Reinforcement Learning: Challenges and Future Directions

LLM-Based Multi-Agent Decision-Making: Challenges and Future Directions

In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poetry writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Decision-Making (DM) and achieve decent res

Retrieval-Augmented Hierarchical in-Context Reinforcement Learning and Hindsight Modular Reflections for Task Planning with LLMs

Large Language Models (LLMs) have demonstrated remarkable abilities in various language tasks, making them promising candidates for decision-making in robotics. Inspired by Hierarchical Reinforcement Learning (HRL), we propose Retrieval-Augmented Hierarchical in-context reinforcement Learning (RAHL)

HMAAC: Hierarchical Multi-Agent Actor-Critic for Aerial Search with Explicit Coordination Modeling

Unmanned Aerial Vehicles (UAVs) have become prevalent in Search-And-Rescue (SAR) missions. However, existing solutions to the control and coordination of UAV s are mostly limited to specific environments and are not robust to handle unreliable/unstable communications. To deal with these challenges,…

On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator sy…

Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anoma…

Light-Weight Object Detection and Decision Making via Approximate Computing in Resource-Constrained Mobile Robots

Most of the current solutions for autonomous flights in indoor environments rely on purely geometric maps (e.g., point clouds). There has been, however, a growing interest in supplementing such maps with semantic information (e.g., object detections) using computer vision algorithms. Unfortunately,…