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.
