Programming by Demonstration of Robot Manipulators
To my family
Örebro Studies in Technology 34
Programming by Demonstration of
© Alexander Skoglund, 2009
Title: Programming by Demonstration of Robot Manipulators
Publisher: Örebro University 2009
Editor: Jesper Johanson
Printer: Intellecta Infolog, V Frölunda 05/2009
If a non-expert wants to program a robot manipulator he needs a natural inter-
face that does not require rigorous robot programming skills. Programming-by-
demonstration (PbD) is an approach which enables the user to program a robot
by simply showing the robot how to perform a desired task. In this approach,
the robot recognizes what task it should perform and learn how to perform it
by imitating the teacher.
One fundamental problem in imitation learning arises from the fact that
embodied agents often have different morphologies. Thus, a direct skill transfer
from human to a robot is not possible in the general case. Therefore, a system-
atic approach to PbD is needed, which takes the capabilities of the robot into
account–regarding both perception and body structure. In addition, the robot
should be able to learn from experience and improve over time. This raises the
question of how to determine the demonstrator’s goal or intentions. It is shown
that this is possible–to some degree–to infer from multiple demonstrations.
This thesis address the problem of generation of a reach-to-grasp motion
that produces the same results as a human demonstration. It is also of interest
to learn what parts of a demonstration provide important information about
The major contribution is the investigation of a next-state-planner using
a fuzzy time-modeling approach to reproduce a human demonstration on a
robot. It is shown that the proposed planner can generate executable robot
trajectories based on a generalization of multiple human demonstrations. The
notion of hand-states is used as a common motion language between the human
and the robot. It allows the robot to interpret the human motions as its own,
and it also synchronizes reaching with grasping. Other contributions include
the model-free learning of human to robot mapping, and how an imitation
metric can be used for reinforcement learning of new robot skills.
The experimental part of this thesis presents the implementation of PbD
of pick-and-place-tasks on different robotic hands/grippers. The different plat-
forms consist of manipulators and motion capturing devices.
Keywords: programming-by-demonstration, imitation learning, hand-state,
next-state-planner, fuzzy time-modeling approach.