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
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick, Aaron D. Ames
Task-driven Modular Co-design of Vehicle Control Systems
Gioele Zardini, Zelio Suter, Andrea Censi, Emilio Frazzoli
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
Jiabin Lou, Rong Ding, Wenjun Wu