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

-- 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 V alence-Arousal-Dominance (V AD) spectrum, corroborating the usage of Laban Shape. I. INTRODUCTION With the growing popularity of robots and their increased deployment in the real world, it has become increasingly important to ensure their successful collaboration with humans. Effective human-robot collaboration relies on clear communication and an accurate understanding of the robot's mental model, which refers to the humans' understanding of the robots' capabilities, intentions, and limitations [37].