Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators

TL;DR

This paper introduces a novel approach using Laban notation to generate expressive robotic trajectories that improve human-robot communication by conveying emotions and functional cues, validated through human studies.

Contribution

It presents two new variants of Hesitant expressive motion and enhances existing emotive trajectories with Laban Shape, advancing robot expressivity and interpretability.

Findings

Hesitant motion variants are perceived as less competent.

Enhanced trajectories clearly convey distinct emotions.

Laban Shape effectively improves emotional expressivity.

Abstract

Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offer high precision, versatility, and productivity, they exhibit expressionless and monotonous motions that conceal the robot's intention, resulting in a lack of efficiency and transparency with humans. In this work, we use Laban notation, a dance annotation language, to enable robotic manipulators to generate trajectories with functional expressivity, where the robot uses nonverbal cues to communicate its abilities and the likelihood of succeeding at its task. We achieve this by introducing two novel variants of Hesitant expressive motion (Spoke-Like and Arc-Like). We also enhance the emotional expressivity of four existing emotive trajectories (Happy, Sad, Shy, and Angry) by augmenting Laban Effort usage with Laban Shape. The functionally expressive motions are validated via a human-subjects study, where participants equate both variants of Hesitant motion with reduced robot competency. The enhanced emotive trajectories are shown to be viewed as distinct emotions using the Valence-Arousal-Dominance (VAD) spectrum, corroborating the usage of Laban Shape.