Explores Bayesian disturbance injection for robust imitation in robot learning, demonstrating its effectiveness in reducing error compounding and achieving high task achievement.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Covers the Subsumption Architecture, a layered control system for mobile robots to operate at increasing levels of competence by suppressing lower level outputs.
