Learning Based
Learning-based approaches are revolutionizing various fields by enabling systems to learn complex behaviors and adapt to dynamic environments, primarily aiming to improve efficiency, robustness, and safety. Current research focuses on applying deep reinforcement learning, diffusion models, and Koopman operators to control robots, optimize planning algorithms (like those for pathfinding and task sequencing), and improve the accuracy and efficiency of simulations. These advancements have significant implications for robotics, autonomous systems, and other domains requiring adaptable and intelligent control, offering solutions to challenges in areas such as safe navigation, precise manipulation, and efficient resource allocation.
Papers
Safe and Reliable Training of Learning-Based Aerospace Controllers
Udayan Mandal, Guy Amir, Haoze Wu, Ieva Daukantas, Fletcher Lee Newell, Umberto Ravaioli, Baoluo Meng, Michael Durling, Kerianne Hobbs, Milan Ganai, Tobey Shim, Guy Katz, Clark Barrett
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Rui Jin, Derun Li, Dehui Xiang, Lei Zhang, Hailing Zhou, Fei Shi, Weifang Zhu, Jing Cai, Tao Peng, Xinjian Chen
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi, Hamidreza Mirkhani, Weize Zhang, Cao Tongtong, Kasra Rezaee
Sensitivity-Informed Augmentation for Robust Segmentation
Laura Zheng, Wenjie Wei, Tony Wu, Jacob Clements, Shreelekha Revankar, Andre Harrison, Yu Shen, Ming C. Lin