Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Introduces the basics of robotics, covering definitions, classifications, and statistics, and explores the evolution and applications of different types of robots.
Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
