Jonathan Eden

Jonathan Eden, Mario Bräcklein, Jaime Ibáñez Pereda et al.

Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this vision possible. By allowing individuals to achieve otherwise impossible actions, this movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with their environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning, and propose a vision for the field.

Yanan Li, Jonathan Eden, Gerolamo Carboni et al.

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.