  Many works in robot teaching either focus only on teaching task knowledge,
such as geometric constraints, or motion knowledge, such as the motion for
accomplishing a task. However, to effectively teach a complex task sequence to
a robot, it is important to take advantage of both task and motion knowledge.
The task knowledge provides the goals of each individual task within the
sequence and reduces the number of required human demonstrations, whereas the
motion knowledge contain the task-to-task constraints that would otherwise
require expert knowledge to model the problem. In this paper, we propose a body
role division approach that combines both types of knowledge using a single
human demonstration. The method is inspired by facts on human body motion and
uses a body structural analogy to decompose a robot's body configuration into
different roles: body parts that are dominant for imitating the human motion
and body parts that are substitutional for adjusting the imitation with respect
to the task knowledge. Our results show that our method scales to robots of
different number of arm links, guides a robot's configuration to one that
achieves an upcoming task, and is potentially beneficial for teaching a range
of task sequences.
