Breathless: An 8-hour Performance Contrasting Human and Robot Expressiveness

Fuente: arXiv
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Main Authors: Cuan, Catie, Qiu, Tianshuang, Ganti, Shreya, Goldberg, Ken
Format: Preprint
Published: 2024
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author Cuan, Catie
Qiu, Tianshuang
Ganti, Shreya
Goldberg, Ken
author_facet Cuan, Catie
Qiu, Tianshuang
Ganti, Shreya
Goldberg, Ken
contents This paper describes the robot technology behind an original performance that pairs a human dancer (Cuan) with an industrial robot arm for an eight-hour dance that unfolds over the timespan of an American workday. To control the robot arm, we combine a range of sinusoidal motions with varying amplitude, frequency and offset at each joint to evoke human motions common in physical labor such as stirring, digging, and stacking. More motions were developed using deep learning techniques for video-based human-pose tracking and extraction. We combine these pre-recorded motions with improvised robot motions created live by putting the robot into teach-mode and triggering force sensing from the robot joints onstage. All motions are combined with commercial and original music using a custom suite of python software with AppleScript, Keynote, and Zoom to facilitate on-stage communication with the dancer. The resulting performance contrasts the expressivity of the human body with the precision of robot machinery. Video, code and data are available on the project website: https://sites.google.com/playing.studio/breathless
format Preprint
id arxiv_https___arxiv_org_abs_2411_12361
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Breathless: An 8-hour Performance Contrasting Human and Robot Expressiveness
Cuan, Catie
Qiu, Tianshuang
Ganti, Shreya
Goldberg, Ken
Robotics
Computer Vision and Pattern Recognition
This paper describes the robot technology behind an original performance that pairs a human dancer (Cuan) with an industrial robot arm for an eight-hour dance that unfolds over the timespan of an American workday. To control the robot arm, we combine a range of sinusoidal motions with varying amplitude, frequency and offset at each joint to evoke human motions common in physical labor such as stirring, digging, and stacking. More motions were developed using deep learning techniques for video-based human-pose tracking and extraction. We combine these pre-recorded motions with improvised robot motions created live by putting the robot into teach-mode and triggering force sensing from the robot joints onstage. All motions are combined with commercial and original music using a custom suite of python software with AppleScript, Keynote, and Zoom to facilitate on-stage communication with the dancer. The resulting performance contrasts the expressivity of the human body with the precision of robot machinery. Video, code and data are available on the project website: https://sites.google.com/playing.studio/breathless
title Breathless: An 8-hour Performance Contrasting Human and Robot Expressiveness
topic Robotics
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2411.12361