Laban-Inspired Task-Constrained Variable Motion Generation on Expressive Aerial Robots

Abstract

This paper presents a method for creating expressive aerial robots through an algorithmic procedure for creating variable motion under given task constraints. This work is informed by the close study of the Laban/Bartenieff movement system, and movement observation from this discipline will provide important analysis of the method, offering descriptive words and fitting contexts—a choreographic frame—for the motion styles produced. User studies that use utilize this qualitative analysis then validate that the method can be used to generate appropriate motion in in-home contexts. The accuracy of an individual descriptive word for the developed motion is up to 77% and context accuracy is up to 83%. A capacity for state discernment from motion profile is essential in the context of projects working toward developing in-home robots.

A Generative Model to Embed Human Expressivity into Robot Motions

“…To the best of our knowledge, this is the first work to address expressive transmission in general terms. Previous works [ 9 , 33 , 41 , 70 , 71 , 72 ] relied on expert descriptors or interactive interfaces to enact expressive and emotive behavior. This is why our experimental setup focuses on analyzing the effectiveness of transmitting human expressivity to the robot.…”

“…Our method can understand the expressive qualities of human movement and exploit them to generate a new movement for the robot. Unlike previous approaches where direct manipulation of the robot’s trajectory, control, and motion qualities, e.g., acceleration, velocity, and position, are the essence of the expressive definition [ 33 , 41 , 70 , 71 , 72 ], our method extracts the underlying qualities from the human, and then integrates them into the robot task, generating a new movement for the robot. This allows for a direct interaction between user and robot, and removes the need for expert design morphology-dependent constraints and the constant reprogramming seen in previous methods.…”

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

“…Similarly, if parts of the body, which are usually used to express a certain emotion, are not visible, it is possible that the emotion could still be decoded by identifying the motor elements composing the visible part of the movement. Moreover, by slightly changing the kinematics of a movement of a robot or animation (i.e., adding to a gesture or a functional movement the kinematics of certain LMA motor elements associated with a specific emotion), we can "color" this functional movement with an expression of that emotion, even when the movement is not the typical movement for expressing that emotion (e.g., [125] and [126]). Similarly, identifying the quality of a movement can enable decoding the expressed emotion even from functional actions, such as walking [127] or reaching and grasping.…”

The Heuristic of Directional Qualitative Semantic: A New Heuristic for Making Decisions about Spinning with Qualitative Reasoning

“…One proposal to carry out qualitative reasoning about orientation and spinning for agents is based on OP RA m with adjustable granularity [19]. Other proposals include the use of a topological-semantic-metric (TSM) map [14] and the Qualitative Trajectory Calculus Double-Cross [20].…”