A Contact-Adaptive Control Framework for Co-Manipulation Tasks with Application to Collaborative Screwing

This paper proposes a novel framework for robotic
manipulation tasks, exploiting the Human-Robot Collaboration
(HRC) potential. The framework integrates two adaptive controllers
to i) modulate robot compliance in contact with the
environment along constrained directions, and to ii) enable
human guidance through touch when a manual intervention
is needed. To demonstrate the potential of the proposed framework,
we consider a collaborative screwing task. In this example
application, the operator is in charge of placing the screws on
the table and following the instructions on a graphical user
interface. The robot, after identifying the position of the screws
through an online human pose-tracking system, performs the
screwing using the proposed controller. The human operator
can adjust the screwing position of the robot using the adaptive
interface at anytime if the position accuracy through vision is
insufficient. We first experimentally evaluate the operation of
the proposed controller and demonstrate its performance in
comparison to the classical impedance control. Next, the overall
system is evaluated in a collaborative (human and robot) setting.