Breathless: An 8-hour Performance Contrasting Human and Robot Expressiveness
This work addresses the problem of artistic expression in robotics for performance art, but it is incremental as it applies existing methods to a new creative context.
The paper tackles the challenge of creating an expressive robot performance by combining sinusoidal motions, deep learning-based human-pose tracking, and live improvised robot motions to contrast human and robot expressivity in an eight-hour dance.
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