Autonomous System
Autonomous systems research focuses on developing machines capable of operating independently and achieving goals without continuous human intervention. Current research emphasizes improving robustness and safety through techniques like vulnerability-adaptive protection, advanced control algorithms (including model predictive control and reinforcement learning), and the use of diverse sensor modalities (e.g., dynamic vision sensors, LiDAR) integrated with sophisticated model architectures such as neural networks and transformers. This field is crucial for advancing safety-critical applications across various sectors, including transportation, robotics, and industrial automation, by enabling more reliable and efficient systems.
Papers
Learning Run-time Safety Monitors for Machine Learning Components
Ozan Vardal, Richard Hawkins, Colin Paterson, Chiara Picardi, Daniel Omeiza, Lars Kunze, Ibrahim Habli
TornadoDrone: Bio-inspired DRL-based Drone Landing on 6D Platform with Wind Force Disturbances
Robinroy Peter, Lavanya Ratnabala, Demetros Aschu, Aleksey Fedoseev, Dzmitry Tsetserukou