Abstract-Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning does not cope with the noise inherent to human demonstrations. Here, we propose an approach for robot learning from demonstration that can generalize noisy task demonstrations to a new goal point and to an environment with obstacles. This strategy for robot learning from demonstration results in a control policy that incorporates different types of learning from demonstration, which correspond to different types of observational learning as outlined in developmental psychology.
Ghalamzan et al. REF proposed a threetiered approach that can generalize noisy task demonstrations to new situations with obstacles.
An incremental approach to learning generalizable robot tasks from human demonstration
2015 IEEE International Conference on Robotics and Automation (ICRA)
2015 IEEE International Conference on Robotics and Automation (ICRA)
Computer Science