A comparison of two algorithms for robot learning from demonstration

Robot learning from demonstration focuses on algorithms that enable a robot to learn a policy from demonstrations performed by a teacher, typically a human expert. This paper presents an experimental evaluation of two learning from demonstration algorithms, Interactive Reinforcement Learning and Behavior Networks. We evaluate the performance of these algorithms using a humanoid robot and discuss the relative advantages and drawbacks of these methods with respect to learning time, number of demonstrations, ease of implementation and other metrics. Our results show that Behavior Networks rely on a greater degree of domain knowledge and programmer expertise, requiring very precise definitions for behavior pre- and post-conditions. By contrast Interactive RL requires a relatively simple implementation based only on the robot's sensor data and actions. However, Behavior Networks leverage the pre-coded knowledge to effectively reduce learning time and the required number of human interactions to learn the task.