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
GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
Hsin-Jung Yang, Joe Beck, Md Zahid Hasan, Ekin Beyazit, Subhadeep Chakraborty, Tichakorn Wongpiromsarn, Soumik Sarkar
Ensuring Safe Autonomy: Navigating the Future of Autonomous Vehicles
Patrick Wolf
Risk-Aware Robotics: Tail Risk Measures in Planning, Control, and Verification
Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Lars Lindemann, Margaret P. Chapman, George J. Pappas, Aaron D. Ames, Joel W. Burdick
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
SE(3) Linear Parameter Varying Dynamical Systems for Globally Asymptotically Stable End-Effector Control
Sunan Sun, Nadia Figueroa