  This paper introduces human-robot sensory augmentation and illustrates it on
a tracking task, where performance can be improved by the exchange of sensory
information between the robot and its human user. It was recently found that
during interaction between humans, the partners use each other's sensory
information to improve their own sensing, thus also their performance and
learning. In this paper, we develop a computational model of this unique human
ability, and use it to build a novel control framework for human-robot
interaction. The human partner's control is formulated as a feedback control
with unknown control gains and desired trajectory. A Kalman filter is used to
estimate first the control gains and then the desired trajectory. The estimated
human partner's desired trajectory is used as augmented sensory information
about the system and combined with the robot's measurement to estimate an
uncertain target trajectory. Simulations and an implementation of the presented
framework on a robotic interface validate the proposed observer-predictor pair
for a tracking task. The results obtained using this robot demonstrate how the
human user's control can be identified, and exhibit similar benefits of this
sensory augmentation as was observed between interacting humans.
