Interacting humans and robots can improve sensory prediction by adapting their viscoelasticity

TL;DR

This paper introduces a computational model and optimal control strategy that enhances haptic communication and collaboration between humans and robots by adaptively tuning viscoelasticity based on sensory and motor noise.

Contribution

It develops the SOIE controller that predicts and improves haptic information exchange through adaptive viscoelasticity tuning in human-robot interactions.

Findings

The SOIE controller outperforms stiff or compliant controls in robot-robot tracking tasks.

Humans adapt muscle activation to optimize haptic communication based on noise.

Robots using the SOIE controller show improved tracking and communication in human-robot experiments.

Abstract

To manipulate objects or dance together, humans and robots exchange energy and haptic information. While the exchange of energy in human-robot interaction has been extensively investigated, the underlying exchange of haptic information is not well understood. Here, we develop a computational model of the mechanical and sensory interactions between agents that can tune their viscoelasticity while considering their sensory and motor noise. The resulting stochastic-optimal-information-and-effort (SOIE) controller predicts how the exchange of haptic information and the performance can be improved by adjusting viscoelasticity. This controller was first implemented on a robot-robot experiment with a tracking task which showed its superior performance when compared to either stiff or compliant control. Importantly, the optimal controller also predicts how connected humans alter their muscle activation to improve haptic communication, with differentiated viscoelasticity adjustment to their own sensing noise and haptic perturbations. A human-robot experiment then illustrated the applicability of this optimal control strategy for robots, yielding improved tracking performance and effective haptic communication as the robot adjusted its viscoelasticity according to its own and the user's noise characteristics. The proposed SOIE controller may thus be used to improve haptic communication and collaboration of humans and robots.