Improving Tracking through Human-Robot Sensory Augmentation
This addresses the problem of enhancing human-robot collaboration for tasks like tracking, though it appears incremental as it adapts known human-human sensory augmentation concepts to robotics.
The paper tackles the problem of improving tracking performance in human-robot interaction by developing a computational model and control framework for sensory augmentation, where the robot estimates the human's desired trajectory to enhance tracking, with simulations and robotic implementation validating the approach and showing benefits similar to human-human interaction.
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.