Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Introduces the basics of robotics, covering definitions, classifications, and statistics, and explores the evolution and applications of different types of robots.
Presents a novel architecture for robot learning of haptic interaction, achieving robust object class estimation and enhancing haptic interaction efficiency.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.
Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.
Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.
