Human Error Identification in Programming by Demonstration of Compliant Motion Robotic Tasks

In this paper, we suggest a scheme for error
identification in human skills transfer when using the
Programming by Demonstration (PbD) in adding a set of skills
from a human operator to the force- controlled robotic tasks.
Such errors in human skills transfer is majorly caused from the
difficulty of properly synchronizing the human and machine
responses. Based on the captured Cartesian forces and torques
signals of the manipulated object, we present an approach of
identifying the errors stemmed from human wrong skills transfer
in a PbD process. The scheme is composed of using the
Gravitational Search- Fuzzy Clustering Algorithm (GS-FSA) in
finding the centroid of the captured forces and torques signals
for each Contact Formation (CF). Then using a distance- based
outlier identification approach along with the centroid of each
signal, the human errors can be identified in the framework of
data outlier identification. In order to validate the approach, a
test stand, composed of a KUKA Light Weight Robot
manipulating a rigid cube object, is built. The manipulated
object is assumed to interact with an environment composed of
three orthogonal planes. Error identification for two case studies
will be considered and other cases can be dealt with in a similar
manner. From the experimental results, excellent human error
identification is shown when using the suggested approach.