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
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Jianwen Li, Hyunsang Park, Wenjian Hao, Lei Xin, Jalil Chavez-Galaviz, Ajinkya Chaudhary, Meredith Bloss, Kyle Pattison, Christopher Vo, Devesh Upadhyay, Shreyas Sundaram, Shaoshuai Mou, Nina Mahmoudian
Providing Safety Assurances for Systems with Unknown Dynamics
Hao Wang, Javier Borquez, Somil Bansal