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
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows
Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray
Evaluation Metrics for Object Detection for Autonomous Systems
Apurva Badithela, Tichakorn Wongpiromsarn, Richard M. Murray