Laban head-motions convey robot state: A call for robot body language

Abstract

Functional robots are an increasing presence in shared human-machine environments. Humans efficiently parse motion expressions, gaining an immediate impression of an agent's current action and state. Past work has shown that motion can effectively reveal a robot's current task objective to bystanders and collaborators, however, the layering of expression on pre-existing robot task motions has yet to be explored. Rather than showing us what the robot is doing, these layered motion characteristics leverage the how of the task motions to convey additional robot attitudes, e.g., confidence, adherence to deadline or flexibility of attention. To lay the foundations for this objective, we adapt the Laban Efforts, a system from dance and acting training in use for over 50 years. We operationalize features representing the four Laban Efforts (Time, Space, Weight, and Flow) to the movements of a 2-DOF Nao head and a 4-DOF Keepon robot during simple dance and look-for-someone behaviors. Using online survey, we collect 1028 motion ratings for 72 robot motion videos depicting contrasting Effort motion examples. We achieve statistically significant legibility results for all four Effort implementations. Even without human degrees of freedom, we find that robot motion patterns can convey complex expressions to people.