Robot Learning From Demonstration

The goal of robot learning from demonstration is to have a robot learn from watching a demonstration of the task to be performed. In our approach to learning from demonstration the robot learns a reward function from the demonstration and a task model from repeated attempts to perform the task. A policy is computed based on the learned reward function and task model. Lessons learned from an implementation on an anthropomorphic robot arm using a pendulum swing up task include 1 simply mimicking demonstrated motionsis notadequate toperform this task, 2 a task planner can use a learned model and reward function to compute an appropriate policy, 3 this modelbased planning process supports rapid learning, 4 both parametric and nonparametric models can be learned and used, and 5 incorporating a task level direct learning component, which is non-model-based, in addition to the model-based planner, is useful in compensating for structural modeling errors and slow model learning. 1 LEARNING FR...

• Type: Conference
• Year: 1997
• Where: ICML
• Authors: Christopher G. Atkeson, Stefan Schaal
• Keywords: Anthropomorphic Robot Arm, ICML 1997, Machine Learning, Task Model, Task Planner