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

2025-05-16

AI Summary

In human-robot collaboration, insufficient expression of robotic motion intent undermines communication transparency. Method: This paper proposes a dual-objective trajectory generation method grounded in the Shape dimension of Laban Movement Analysis (LMA): (1) it introduces two novel “hesitant” functional motions—Spoke-Like and Arc-Like—to explicitly convey uncertainty; and (2) it pioneers the integration of LMA Shape with Effort parameters to enhance discriminability of Happy, Sad, Shy, and Angry emotions within the Valence-Arousal-Dominance (VAD) space. Contribution/Results: Human-subject experiments demonstrate that hesitant motions significantly reduce human overestimation of robot capability (p < 0.05); emotion-laden trajectories achieve statistically significant separation in VAD space (p < 0.01), confirming that LMA Shape substantially enhances affective expressivity. This work establishes an interpretable, empirically evaluable paradigm for synergistic functional and emotional embodiment in intelligent agents.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Enhancing robotic manipulator motions with emotional and functional expressivity
• Using Laban notation to improve human-robot communication through expressive trajectories
• Validating expressive motions via human perception of robot competency and emotions

Innovation

• Using Laban notation for expressive robotic trajectories
• Introducing Spoke-Like and Arc-Like hesitant motion variants
• Augmenting Laban Effort with Laban Shape for emotions