  Learning from Demonstration (LfD) provides an intuitive and fast approach to
program robotic manipulators. Task parameterized representations allow easy
adaptation to new scenes and online observations. However, this approach has
been limited to pose-only demonstrations and thus only skills with spatial and
temporal features. In this work, we extend the LfD framework to address
forceful manipulation skills, which are of great importance for industrial
processes such as assembly. For such skills, multi-modal demonstrations
including robot end-effector poses, force and torque readings, and operation
scene are essential. Our objective is to reproduce such skills reliably
according to the demonstrated pose and force profiles within different scenes.
The proposed method combines our previous work on task-parameterized
optimization and attractor-based impedance control. The learned skill model
consists of (i) the attractor model that unifies the pose and force features,
and (ii) the stiffness model that optimizes the stiffness for different stages
of the skill. Furthermore, an online execution algorithm is proposed to adapt
the skill execution to real-time observations of robot poses, measured forces,
and changed scenes. We validate this method rigorously on a 7-DoF robot arm
over several steps of an E-bike motor assembly process, which require different
types of forceful interaction such as insertion, sliding and twisting.
