  This paper presents, for the first time, a method for learning in-contact
tasks from a teleoperated demonstration with a hydraulic manipulator. Due to
the use of extremely powerful hydraulic manipulator, a force-reflected
bilateral teleoperation is the most reasonable method of giving a human
demonstration. An advanced subsystem-dynamic-based control design framework,
virtual decomposition control (VDC), is used to design a stability-guaranteed
controller for the teleoperation system, while taking into account the full
nonlinear dynamics of the master and slave manipulators. The use of fragile
force/ torque sensor at the tip of the hydraulic slave manipulator is avoided
by estimating the contact forces from the manipulator actuators' chamber
pressures. In the proposed learning method, it is observed that a
surface-sliding tool has a friction-dependent range of directions (between the
actual direction of motion and the contact force) from which the manipulator
can apply force to produce the sliding motion. By this intuition, an
intersection of these ranges can be taken over a motion to robustly find a
desired direction for the motion from one or more demonstrations. The compliant
axes required to reproduce the motion can be found by assuming that all motions
outside the desired direction is caused by the environment, signalling the need
for compliance. Finally, the learning method is incorporated to a novel
VDC-based impedance control method to learn compliant behaviour from
teleoperated human demonstrations. Experiments with 2-DOF hydraulic manipulator
with a 475kg payload demonstrate the suitability and effectiveness of the
proposed method to perform learning from demonstration (LfD) with heavy-duty
hydraulic manipulators.
