Imitation Learning by Model-based Probabilistic Trajectory Matching
published: Aug. 6, 2013, recorded: April 2013, views: 2865
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to enable it to match its behavior to a teacher's demonstration of the task at hand. This approach is known as imitation learning. Classical methods of imitation learning suffer from the correspondence problem, i.e., when the actions of the teacher are not directly observed or the anatomy of the teacher and the robot differ substantially. To address the correspondence problem, we propose to learn a robot-specific controller that directly matches robot trajectories with demonstrated ones. We use long-term predictions from a learned probabilistic model of the robot's forward dynamics to match the predicted trajectory distribution with the distribution over observed expert trajectories by minimizing the Kullback-Leibler divergence between these trajectory distributions. The power of the resulting approach is demonstrated by imitating human behavior with a tendon-driven, compliant robotic arm with complex dynamics.
Download slides: machine_deisenroth_imitation_learning_01.pdf (2.3 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !