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.
Explores advancements in robot learning for autonomy at scale, covering deep learning challenges, efficient architecture, benchmarking results, and societal implications.
Introduces the basics of robotics, covering definitions, classifications, and statistics, and explores the evolution and applications of different types of robots.
